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A 90-day SEO playbook for AI-driven search visibility

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A 90-day SEO playbook for AI-driven search visibility

SEO now sits at an uncomfortable intersection at many organizations.

Leadership wants visibility in AI-driven search experiences. Product teams want clarity on which narratives, features, and use cases are being surfaced. Sales still depends on pipeline.

Meanwhile, traditional rankings, traffic, and conversions continue to matter. What has changed is the surface area of search.

Pages are now summarized, excerpted, and cited in environments where clicks are optional and attribution is selective. 

When a generative AI summary appears on the SERP, users click traditional result links only about 8% of the time.

As a result, SEO teams need a clearer playbook for earning visibility inside generative outputs, not just around them.

This 90-day action plan outlines how to achieve this in a phased, weekly execution, with practical adjustments tailored to the specific purpose of the website.

Phase 1: Foundation (Weeks 1-2)

Define your ‘AI search topics’

Keywords still matter. But AI systems organize information around entities, topics, and questions, not just query strings.

The first step is to decide what you want AI tools to associate your brand with.

Action steps

  • Identify 5-10 core topics you want to be known for.
  • For each topic, map:
    • The questions users ask most often
    • The comparisons they evaluate
    • “Best,” “how,” and “why” queries that indicate decision-making intent

Example:

  • Topic: AI SEO tools
  • Mapped query types:
    • Core questions: What are the best AI SEO tools? How does AI improve SEO?
    • Comparisons: AI SEO tools vs traditional SEO tools.
    • Intent signals: Best AI SEO tools for content optimization.

Where this shifts by website type

  • Content hubs (media brands, publishers, research orgs) should prioritize mapping educational breadth – covering a topic comprehensively so AI systems see the site as a reference source, not a transactional endpoint.
  • Services/lead gen sites (agencies, consultants, local businesses) should map problem-solution queries prospects ask before converting, especially comparison and “how does this work?” questions.
  • Product and ecommerce sites (DTC brands, marketplaces, subscription ecommerce, retailers) should map topics to use cases, alternatives, and comparisons – not just product names or category terms.
  • Commercial, long-funnel sites (B2B SaaS, fintech, healthcare) should anchor topics to category leadership – the “what is,” “how it works,” and “why it matters” content buyers research long before demos.

If you can’t clearly articulate what you want AI systems to associate you with, neither can they.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

Create AI-friendly content structure

Generative engines consistently surface content that is easy to extract, summarize, and reuse. 

In practice, that favors pages where answers are clearly framed, front-loaded, and supported by scannable structure.

 High-performing pages tend to follow a predictable pattern.

AI-friendly content structures include: 

  • A short intro (2-3 lines) that establishes scope.
  • A direct answer placed immediately after the header, written to stand alone if excerpted.
  • Bulleted lists or numbered steps that break down the explanation.
  • A concise FAQ section at the bottom that reinforces key queries.

This increases the likelihood your content is:

  • Quoted in AI Overviews.
  • Used in ChatGPT or Perplexity answers.
  • Surfaced for voice and conversational search.

For ecommerce and services sites in particular, this is often where internal resistance shows up. Teams worry that answering questions too directly will reduce conversion opportunities. 

In AI-driven search, the opposite is usually true: pages that make answers easy to extract are more likely to be surfaced, cited, and revisited when users move from research to decision-making.

Dig deeper: Organizing content for AI search: A 3-level framework

Phase 2: Generative engine optimization (Weeks 3-6)

Optimize for AI answers (GEO/AEO)

In generative search, content that gets surfaced typically resolves the core question immediately, then provides context and depth. 

For many commercial teams, that requires rethinking how early pages prioritize explanation versus persuasion – a shift that’s increasingly necessary to earn visibility at all.

This is where GEO (generative engine optimization) and AEO (answer engine optimization) move from theory into page-level execution.

  • Add a 1–2 sentence TL;DR under key H2s that can stand on its own if excerpted
  • Use explicit, question-based headers:
    • “What is…”
    • “How does…”
    • “Why does…”
  • Include clear, plain-language definitions before introducing nuance or positioning

Example:

What is generative engine optimization?

Generative engine optimization (GEO) helps content get selected as a source in AI-generated answers.

In practice, GEO is the process of structuring and optimizing content so AI tools like ChatGPT and Google AI Overviews can interpret, evaluate, and reference it when responding to user queries.

How does answer-first structure change by site type?

  • Publishers benefit from definitional clarity because it increases citation frequency.
  • Lead gen sites see stronger mid-funnel engagement when prospects get clear answers upfront.
  • Product sites reduce friction by addressing comparison and “is it right for me?” questions early.
  • B2B platforms establish category authority long before a buyer ever hits a pricing page.

Add structured data (high impact, often underused)

Structured data remains one of the clearest ways to signal meaning and credibility to AI-driven search systems. 

It helps generative engines quickly identify the source, scope, and authority behind a piece of content – especially when deciding what to cite.

At a minimum, most sites should implement:

  • Article schema to clarify content type and topical focus.
  • Organization schema to establish the publishing entity.
  • Author or Person schema to surface expertise and accountability.

FAQ schema, where it reflects genuine question-and-answer content, can still reinforce structure and intent – but it should be used selectively, not as a default.

This matters differently by site type:

  • Content hubs benefit when author and publication signals reinforce editorial credibility and reference value.
  • Lead gen and services sites use schema to connect expertise to specific problem areas and queries.
  • Product and ecommerce sites help AI systems distinguish between informational content and transactional pages.
  • Commercial, long-funnel sites rely on schema to support trust signals alongside relevance in high-stakes categories.

Structured data doesn’t guarantee inclusion – but in generative search environments, its absence makes exclusion more likely.

Get the newsletter search marketers rely on.

See terms.

Phase 3: Authority and trust (Weeks 7-10)

Strengthen E-E-A-T signals

As generative systems decide which sources to reference, demonstrated experience increasingly outweighs polish alone. 

Pages that surface consistently tend to show clear evidence that the content comes from real people with real expertise. 

Meaning, signals associated with E-E-A-T – experience, expertise, authoritativeness, and trust – remain central to how generative systems decide which sources to reference.

Key signals to reinforce:

  • Clear author bios that establish credentials, role, or subject-matter relevance.
  • First-hand experience statements that indicate direct involvement (“We tested…”, “In our experience…”).
  • Original visuals, screenshots, data, or case studies that can’t be inferred or synthesized

This is where generic, AI-generated content reliably falls short. 

Without visible signals of experience and accountability, AI systems struggle to distinguish authoritative sources from interchangeable ones.

How different site types should demonstrate experience and authority

  • Media and research sites should reinforce editorial standards, sourcing, and author attribution to support citation trust.
  • Agencies and consultants benefit from foregrounding lived client experience and specific outcomes, not abstract expertise.
  • Ecommerce brands earn trust through real-world product usage, testing, and visual proof.
  • High-ACV B2B companies stand out by showcasing practitioner insight and operational knowledge rather than marketing language alone.

If your content reads like it could belong to anyone, AI systems will treat it that way.

Dig deeper: User-first E-E-A-T: What actually drives SEO and GEO

Build ‘citation-worthy’ pages

Certain page types are more likely to be cited in AI-generated answers because they organize information in ways that are easy to extract, compare, and reference. 

These pages are designed to serve as reference material – resolving common questions clearly and completely, rather than advancing a particular perspective.

Formats that consistently perform well include:

  • Ultimate guides that consolidate a topic into a single, authoritative resource.
  • Comparison tables that make differences explicit and scannable.
  • Statistics pages that centralize data points AI systems can reference.
  • Glossaries that define terms clearly and consistently.

Pages with titles such as “AI SEO Statistics (2025)” or “Best AI SEO Tools Compared” are frequently surfaced because they signal completeness, recency, and reference value at a glance.

For commercial sites, citation-worthy pages don’t replace conversion-focused assets. 

They support them by capturing early-stage, informational demand – and positioning the brand as a credible source long before a buyer enters the funnel.

Dig deeper: How generative engines define and rank trustworthy content

Phase 4: Multimodal SEO (Weeks 11-12)

Optimize beyond text

Generative systems increasingly synthesize signals across text, images, and video when assembling answers. 

Content that performs well in AI-driven search is often reinforced across formats, not confined to a single page or medium.

  • Add descriptive, specific alt text that explains what an image shows and why it’s relevant.
  • Create short-form videos paired with transcripts that mirror on-page explanations.
  • Repurpose core content into formats AI systems can encounter and contextualize elsewhere:
    • YouTube videos.
    • LinkedIn carousels.
    • X threads.

How this supports different site goals

  • Publishers extend the reach and reference value of core reporting and explainers.
  • Services and B2B sites reinforce expertise by repeating the same answers across multiple surfaces.
  • Ecommerce brands support discovery by contextualizing products beyond traditional listings and category pages.

Track AI visibility – not just traffic

As generative results absorb more of the discovery layer, traditional click-based metrics capture only part of search performance. 

AI visibility increasingly shows up in how often – and where – a brand’s content is referenced, summarized, or surfaced without a click.

With 88% of businesses worried about losing organic visibility in the world of AI-driven search, tracking these signals is essential for demonstrating continued influence and reach.

Signals worth monitoring include:

  • Featured snippet ownership, which often feeds AI-generated summaries.
  • Appearances within AI Overviews and similar answer experiences.
  • Brand mentions inside AI tools during exploratory queries.
  • Search Console impressions, even when clicks don’t follow.

For long sales cycles in particular, these signals act as early indicators of influence. 

AI citations and impressions often precede direct engagement, shaping consideration well before a buyer enters the funnel.

Dig deeper: LLM optimization in 2026: Tracking, visibility, and what’s next for AI discovery

Recommended tools

These tools support different parts of an SEO-for-AI workflow, from topic research and content structure to schema implementation and visibility tracking.

  • Content and AI SEO 
    • Surfer, Clearscope, Frase
    • Used to identify gaps in topical coverage and evaluate whether content resolves questions clearly enough to be excerpted in AI-generated answers.
  • Schema and structured data 
    • RankMath, Yoast, Schema App
    • Useful for implementing and maintaining schema that helps AI systems interpret content, authorship, and organizational credibility.
  • Visibility and performance tracking 
    • Google Search Console, Ahrefs
    • Essential for monitoring impressions, query patterns, and how content surfaces in search – including cases where visibility doesn’t result in a click.
  • AI research and validation 
    • ChatGPT, Perplexity, Gemini
    • Helpful for testing how topics are summarized, which sources are cited, and where your content appears (or doesn’t) in AI-driven responses.

The rule that matters most

AI systems tend to favor content that provides definitive answers to questions. 

If your content can’t answer a question clearly in 30 seconds, it’s unlikely to be selected for AI-generated answers.

What separates teams succeeding in this environment isn’t experimentation with new tactics, but consistency in execution. 

Pages built to be understandable, referenceable, and trustworthy are the ones generative systems return to.

Read more at Read More

Fashion AI SEO: How to Improve Your Brand’s LLM Visibility

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AI chat is changing how people shop for fashion — fast.

Before AI, buying something as simple as casual leggings meant typing keywords into Google. Then, sifting through pages of results.

Comparing prices. Reading reviews. Getting overwhelmed.

In fact, 74% of shoppers give up because there’s too much choice, according to research by Business of Fashion and McKinsey.

Now?

A shopper submits a query. AI gives one clear answer — often with direct links to products, reviews, and retailers. They can even click straight to purchase.

Google AI Mode – Women's leggings

So, how do you make sure AI recommends your fashion brand?

We analyzed how fashion brands appear in AI search. And why some brands dominate while others disappear.

In this article, you’ll learn how large language models (LLMs) interpret fashion, what drives visibility, and the levers you can pull to get your brand visible in AI searches (plus a free fashion trend calendar to help you plan).

Note: The data in this article comes from Semrush’s AI Visibility Index, August 2025.

The 3 Types of AI Visibility in Fashion

There are three ways people will see your brand in AI search: brand mentions, citations, and recommendations.

3 Types of AI Visibility in Fashion

Brand mentions are references to your brand within an answer.

Ask AI about the latest fashion trends, and the answer includes a couple of relevant brands.

ChatGPT – Top trending fashion looks – Brands

Citations are the proof that backs up AI answers. Your brand properties get linked as a source. This could be product pages, sizing guides, or care instructions.

AI Search Visibility

Citations also include other sites that talk about your brand, like Wikipedia, Amazon, or review sites.

Product recommendations are the most powerful form of AI visibility. Your brand isn’t just mentioned; it’s actively suggested when someone is ready to buy.

For example, I asked ChatGPT for recommendations of aviator sunglasses:

ChatGPT – Aviator sunglasses recommendations

Ray-Ban doesn’t just show up as a mention — they’re a recommended option with clickable shopping cards.

How AI Models Choose Which Fashion Brands to Surface

If you’ve ever wondered how AI chooses which fashion brands to surface, here are the two basic factors:

  • By evaluating what other people say about you online
  • By checking how consistently factual and trustworthy your own information is

Let’s talk about consensus and consistency. Plus, we’ll discuss real fashion brands that are winning at both.

Consensus

If you ask all your friends for their favorite ice cream shop, they’ll probably give different answers.

But if almost everyone coincided in the same answer, you trust that’s probably the best place to go.

AI does something similar.

First, it checks different sources of information online. This includes:

  • Editorial websites, like articles in Vogue, Who What Wear, InStyle, and others
  • Community and creator content, including TikTok try-ons, Reddit threads, and YouTube product roundups
  • Retailer corroboration, like ratings and reviews on Amazon, Nordstrom, Zalando, and more
  • Sustainability verification from third parties like B Corp, OEKO-TEX, or Good On You

After analyzing this information, it gives you recommendations for what it perceives to be the best option.

Here’s an example of what that consensus looks like for a real brand:

Brand Consensus

Carhartt is mentioned all over the web. They appear in retail listings, editorial pieces, and in community discussions.

The result?

They get consistent LLM mentions.

ChatGPT – Jacket recommendations

Consistency

AI also judges your brand based on the consistency of your product information.

This includes:

  • Naming & colorways: Identical names/color codes across your own site, retailers, and mentions
  • Fit & size data: Standardized size charts, fit guides, and model measurements
  • Materials & care: The same composition and instructions across all channels
  • Imagery/video parity: The same SKU visuals (like hero, 360, try-on) on your site and retailer sites
  • Price & availability sync: Real-time updates during drops or restocks to avoid stale or conflicting data

For example, Lululemon does a great job of keeping product availability updated on their website.

If you ask AI where to find a specific product type, it directs you back to the Lululemon website.

Google AI Mode – Specific product type

This happens because Lululemon’s site provides accurate, up-to-date information.

Plus, it’s consistent across retailer pages.

The Types of Content That Dominate Fashion AI Search

Mentions get you into the conversation. Recommendations make you the answer. Citations build the credibility that supports both.

The brands winning in AI search have all three — here’s how to diagnose where you stand.

AI Visibility Diagnostic

Let’s talk about the fashion brands that are consistently showing up in AI search results, and the kind of content that helps them gain AI visibility.

Editorial Shopping Guides and Roundups

Editorial content has a huge impact on results.

Sites like Vogue, Who What Wear, and InStyle are regularly cited by LLMs.

TOP Sources Analysis Fashion & Apparel

These editorial pieces are key for AI search, since they frame products in context — showing comparison, specific occasions, or trends.

There are two ways to play into this.

First, you can develop relationships with editorial websites relevant to your brand.

Start by researching your top three competitors. Using Google (or a quick AI search), find out which publications have featured those competitors recently.

Then, reach out to the editor or writers at those publications.

If they’re individual creators, you might send sample products for them to review.

Looking for mentions from bigger publications?

You might consider working with a PR team to get your products listed in articles.

To build consistency in that content, provide data sheets with information about material, fit, or care.

Who What Wear – Provide information

​​

Second, you can build your own editorial content.

That’s exactly what Huckberry does:

Huckberry – Build your own editorial content

They regularly produce editorial-style content that answers questions.

Many of these posts include a video as well, giving them more opportunity for discovery in LLMs:

YouTube – Huckberry wardrobe 2025

Retailer Product Pages and Brand Stores

Think of your product detail page (PDP) as the source of truth for AI.

If you don’t have all the information there, AI will take its answers from other sources — whether or not they’re accurate.

Product pages (your own website or a retailer’s) need to reflect consistent, accurate information. Then, AI can understand and translate into answers.

Some examples might include:

  • Structured sizing information
  • Consistent naming and colorways
  • Up-to-date prices and availability
  • Ratings (with pictures)
  • Fit guides (like sizing guides and images with model measurements and sizing)
  • Materials and care pages
  • Transparent sustainability modules

For example,Everlane provides the typical sizing chart on each of its products. But they take it a step further and include a guide to show how a piece is meant to fit on your body.

You can even see instructions to measure yourself and find the right size.

Everlane – Size Guide

That’s why, when I ask AI to help me pick the right size for a pair of pants, it gives me a clear answer.

And the citations come straight from Everlane’s website.

ChatGPT – Suggesting a size

Everlane’s product pages also include model measurements and sizing.

So when I ask ChatGPT for pictures to help me pick the right size, I get this response:

ChatGPT – Pictures to help

However you choose to present this information on your product pages, just remember: It needs to be identical on all retailer pages as well.

Otherwise, your brand could confuse the LLMs.

User Generated Video Content

What you say about your own brand is one thing.

But what other people say about you online can have a huge influence on your AI mentions.

Of course, you don’t have full control over what consumers post about you online.

So, proactively build connections with creators. Or, try to join the conversation online when appropriate.

This can help you build a positive sentiment toward your brand, which AI will pick up on.

Not sure which creators to work with?

Try searching for your competitors on channels like TikTok or Instagram. See which creators are mentioning their products, and getting engagement.

You can also use tools like Semrush’s Influencer Analytics app to discover influencers.

Search by social channels, and filter by things like follower count, location, and pricing.

Semrush Influencer Analytics App

Here’s an example: Aritzia has grown a lot on TikTok. They show up in creator videos, fit checks, and unboxing-style videos.

In fact, the hashtag #aritziahaul has a total of 32k posts, racking up 561 million views overall.

TikTok – Artizia

Other fashion brands, like Quince, include a reviewing system on their PDPs.

This allows consumers to rate the fit and add pictures of themselves wearing the product.

LLMs also use this information to answer questions.

Quince – Reviwing system

Creator try-ons, styling videos, and similar content can help increase brand mentions in “best for [body type]” or “best for [occasion]” prompts.

Pro tip: Zero-click shopping is coming. Perplexity’s “Buy with Pro” and ChatGPT’s “Instant Checkout” hint at a future where AI answers lead straight to one-click purchases. The effects are still emerging, but as with social shopping, visibility wins. So, make sure your brand shows up in the chats that drive buying decisions.

Reddit and Community Threads

Reddit is a major source of information for fashion AI queries.

This includes information about real-world fit, durability, comfort, return experiences, and comparisons.

For example, Uniqlo shows up regularly in Reddit threads and questions about style.

Reddit – Fashion community threads

You can also find real reviews of durability about the products.

Reddit – Real review of durability

As a result, the brand is getting thousands of mentions in LLMs based on Reddit citations.

Plus, this leads to a ton of organic traffic back to the Uniqlo website.

Semrush – AI Visibility – Uniqlo – Cited Sources

Obviously, it’s impossible to completely control the conversation around your brand. So for this to work, there’s one key thing you can’t miss:

Your products need to be truly excellent.

A mediocre product that has a lot of negative sentiment online won’t show up in AI search results.

And no amount of marketing tactics can fool the LLMs.

Further reading: Learn how to join the conversation online with our Reddit Marketing guide.

Lab Tests and Fabric Explainers

This kind of content shows the quality of your products.

It gives LLMs a measurable benchmark to quote on things like pilling or color fastness.

This content could include:

  • “6-month wear” style videos
  • Pages that explain the fabrics and materials used
  • Third party tests
  • Clear care instructions

For example, Quince has an entire page on their website talking about cashmere.

Quince – About cashmere

And in Semrush’s AI Visibility dashboard, you can see this page is one of the top cited sources from Quince’s website.

Semrush – Visibility Overview – Quince – Cited Pages

Another option is to create content that shows tests of your products.

Here’s a great example from a brand that makes running soles, Vibram.

They sponsored pro trail runner Robyn Lesh, and teamed up with Huckberry to lab test some of their shoes.

YouTube – Vibram – Lab test of the product

This kind of content is helping Vibram maintain solid AI visibility.

Visibility Overview – Vibram – AI Visibility

And for smaller brands who don’t have Vibram’s sponsorship budget?

Try doing product testing content with your own team.

For example, have a team member wear a specific product every day for a month, and report back on durability.

Or, bury a piece of clothing underground and watch how long it takes to decompose, like Woolmark did:

Instagram – Woolmark decompose clothing

Get creative, and you’ll have some fun creating content that can also help your brand be more visible.

Want to check your brand’s AI visibility?

Try the AI Visibility Toolkit from Semrush to see where your brand stands in AI search, and learn how to optimize.

Start by checking your AI visibility score. You’ll see how this measures up against the industry benchmarks.

Visibility Overview – Ray-ban – AI Visibility – Industry avg

You can prioritize next steps based on the Topic Opportunities tab.

There, you’ll see topics where your competitors are being mentioned, but your brand is missed.

Visibility Overview – Ray-ban – Topic & Sources

Then, jump to the Brand Perception tab to learn more about your Share of Voice and Sentiment in AI search results.

You’ll also get some clear insights on improvements you can make.

Semrush – Brand Performance – Sentiment & Share of Voice

Comparisons and Alternatives Content

AI loves a good comparison post (and honestly, who doesn’t?). So, creating content that compares your products to other brands is a great way to get more mentions.

This is part of LLM seeding.

It helps you get brand exposure without depending on organic traffic dependence. Plus, it helps level the playing field with bigger competitors.

How does LLM Seeding Work

For instance, Quince is often cited online as a cheaper alternative to luxury clothing.

I asked ChatGPT for affordable cashmere options, and Quince was the first recommendation.

ChatGPT – Affordable cashmere options

So, why is this brand showing up consistently?

One reason is their comparison content.

In each PDP, you’ll see the “Beyond Compare” box, showing specific points of comparison with major competitors.

Quince – Beyond Compare

The right comparisons are handled honestly and tastefully.

Focus on real points of difference (like Quince does with price). Or, show which products are best for certain occasions.

For example: “Our sweaters are great for hiking in the snow. Our competitors’ sweaters are better for indoor activities.”

Comparisons give AI a reason to recommend your fashion brand when someone asks for an alternative.

What This Shift Means for Your Fashion Brand

AI search has changed the way people discover products, and even their path to purchase.

Before, this involved multiple searches, clicking on different websites, or scrolling through forums. Now, you can do this in one simple interface.

So, how is AI changing fashion, and how can your brand adapt?

Editorial, Retailer, and PDP Split

AI search doesn’t treat every source of information equally.

And depending on which model your audience uses, the “default” source of truth can look very different.

ChatGPT leans heavily on editorial and community signals.

It rewards cultural traction — what people are talking about, buying, and loving.

For example, articles like this one from Vogue are a prime source for ChatGPT answers:

Vogue – Fashion trends

Meanwhile, Google’s AI Mode and Perplexity skew toward retailer PDPs.

They look for structured data like price, availability, or fit guides. In other words, they trust whoever has the cleanest, richest product data.

The most visible brands win in both arenas: cultural conversation and PDP completeness.

Here’s What You Can Do

To show up in all major LLMs, you need two parallel pipelines.

  1. Cultural traction: Like press mentions, creator partnerships, and community visibility
  2. Citation-ready proof: For example, complete and accurate PDPs across retailer channels

Here’s an Example: Carhartt

Carhartt is a great example of a brand that’s winning on both sides.

First, they get consistent cultural visibility.

For instance, Vogue reported that the Carhartt WIP Detroit jacket made Lyst’s “hottest product” list. That led to searches for their brand increasing by 410%.

This makes it more likely for LLMs to recommend their products in answers:

Google AI Mode – Womens workwear jacket

This is the kind of loop that works wonders for a fashion brand.

AI TrenD Loop

At the same time, Carhartt is also stocked across a huge range of retailers. You can find them in REI, Nordstrom, Amazon, and Dick’s, plus their own direct-to-consumer website.

So, Google AI Mode has an abundance of PDPs, videos, reviews, and Q&A to cite.

This makes Carhartt extremely “citation-friendly” in both models.

No wonder it has such a strong AI visibility score.

Visibility Overview – Carhartt – AI Visibility

Trend Shocks and Seasonal Volatility

Trend cycles aren’t a new challenge in the fashion industry. But it becomes a bigger challenge to maintain visibility when those trends affect which brands appear in AI search.

Micro-trends pop up all the time, triggering quick shifts in how AI answers fashion queries.

When the trend heats up, LLMs pull in brands that appear online in listicles or TikTok roundups.

ChatGPT – When the trend heats up

And when the trend cools? Those same brands disappear just as quickly.

Here’s What You Can Do

To stay present during each trend swing, you need a content and operations pipeline that speaks in real time to the language models are echoing.

  1. Build a proactive trend calendar: Map your content to seasonal moments, like spring tailoring, fall layers, holiday capsules, back-to-school basics, and so on
  2. Refresh imagery and copy to mirror trend language: Update PDPs, on-site copy, and retailer description to match the phrasing used in cultural content
  3. Create rapid-fire listicles and lookbooks: Listicle-style content, creator videos, and other trend-related mentions can help boost visibility. This includes building your own content and working with creators and publications to feature your product in their content.

Download our Trend Calendar for Fashion Brands to plan ahead for upcoming trends and create content that matches.

Here’s an Example: UGG

Anyone who was around for Y2K may have been shocked to see UGG boots come around again.

But the brand was ready to jump onto the trend and make the most of their moment.

Vogue reported that UGG made Lyst’s “hottest products” list in 2024.

Since then, they’ve been regularly featured in seasonal “winter wardrobe essentials” style roundups.

One analyst found that there had been a 280% increase in popularity for the shoes. Funny enough, that trend seems to be a regular occurrence every year once “UGG season” rolls around.

In fact, on TikTok, the hashtag #uggseason has almost 70k videos.

TikTok – Uggseason videos

UGG stays visible even as seasons trends shift. That’s because the brand is always present in the content streams that LLMs treat as cultural indicators. By partnering with influencers, UGG amplified its presence so effectively that the boots themselves became a moment — something people wanted to photograph, share, and join in on without being asked.

The result?

They have one of the highest AI Visibility scores I saw while researching this article.

Visibility Overview – Ugg – AI Visibility

(As a marketer, I find this encouraging. As a Millennial, I find it deeply disturbing.)

Pro tip: Want to measure the results? Track how often your brand or SKUs appear in new listicles per month, plus how they rank in those roundups. Then use Semrush’s AI Visibility Toolkit to track your brand’s visibility using trend-related prompts.

Sustainability and Proof (Not Claims)

Sustainability has become one of the strongest differentiators for fashion brands in AI search.

But only when brands back it up with verifiable proof.

LLMs don’t reward vague eco-friendly language. Instead, they surface brands with certifications, documentation, and third-party validation.

Models also pull heavily from Wikipedia and third-party certification databases. These pages often act as trust anchors for AI search results.

Here’s What You Can Do

You need to build a clear, credible footprint that models can cite.

  1. Centralize pages on materials, care, and impact: Make them brief, structured, and verifiable. Include materials, sourcing, certifications, and repair/resale info.
  2. Maintain third-party profiles: Keep your certifications up-to-date. This includes things like Fair Trade, Bluesign, B-Corp, GOTs, etc.
  3. Standardize sustainability claims across all retailers: If your DTC site says “Fair Trade Certified” but your Nordstrom PDP doesn’t? Models treat that as unreliable.

Here’s an Example: Patagonia

Patagonia is the ruler of AI visibility with a 21.96% share of voice.

Top 20 Brands Fashion & Apparel

In part, this is because of their incredible dedication to sustainability. They basically own this niche category within fashion.

Patagonia’s sustainability claims are backed up by third-party certifications.

And they’re displayed proudly on each PDP.

Patagonia – Sustainability Certs

They’re also transparent about their efforts to help the environment.

They keep pages like this updated regularly.

Patagonia – Progress This Season

These sustainable efforts aren’t just big talk.

Review sites and actual consumers speak positively online about these efforts.

Gearist – Patagonia Repair Review

They’ve made their claim as a sustainable fashion brand.

So, Patagonia shows up first, almost always, in LLMs when talking about sustainable fashion:

ChatGPT recommends Patagonia

That’s the power of building a sustainable brand.

Make AI Work for Your Fashion Brand

You’ve seen how the top fashion brands earn AI visibility.

The path forward is simple: Consensus + Consistency.

Build consensus by getting people talking: Create shareable content, encourage customer posts, or work with creators and publications.

Build consistency by keeping your product info aligned across your site and retail partners.

To get started, download our Fashion Trend Content Calendar to plan your strategy around seasonal trends.

Want to go deeper? Check out our complete guide to AI Optimization.

The post Fashion AI SEO: How to Improve Your Brand’s LLM Visibility appeared first on Backlinko.

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Top 10 PPC expert columns of 2025 on Search Engine Land

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Top 10 Search Engine Land PPC columns of 2025

PPC didn’t stand still in 2025. It adjusted. These articles resonated because they answered the real questions advertisers are asking: how to stay competitive, cut wasted spend, work with automation instead of against it, and prepare for what’s next.

Below are links to the 10 most-read Search Engine Land PPC columns of 2025, written by our exceptional subject matter experts.

10. Can small businesses compete on Google Ads anymore?

With the right strategy, even the smallest business can stand out, win customers, and make a lasting impact. Here’s how. (By Sophie Logan. Published Sept. 16.)

9. Google Ads optimization: What to stop, start, and continue in 2025

Shift your optimization mindset in 2025 with fresh strategies for keywords, Performance Max, and audience targeting. (By Pauline Jakober. Published Feb. 6.)

8. CPC inflation: How fast are Google Ads costs rising?

CPCs are rising – but how fast? Compare ad cost inflation to consumer price index and see what it means for your ad strategy. (By Mark Meyerson. Published April 16.)

7. The end of SEO-PPC silos: Building a unified search strategy for the AI era

AI-driven search is blurring the line between organic and paid. Learn how uniting SEO and PPC boosts visibility, intent, and brand authority. (By Jen Cornwell. Published Oct. 6.)

6. How to vibe code for PPC: Building a seasonality analysis tool

PPC scripts hit limits. Vibe coding removes the roadblocks. Turn complex seasonal patterns into simple, data-driven planning tools. (By Frederick Vallaeys. Published Aug. 21.)

5. How to write high-performing Google Ads copy with generative AI

Speed up your ad creation process without losing your message. Use generative AI to craft relevant, personalized copy that connects. (By Jason Tabeling. Published Aug. 1.)

4. 7 Google Ads search term filters to cut wasted spend

These filtering tactics help refine your targeting, reduce spend on low-quality clicks, and uncover new keyword opportunities. (By Menachem Ani. Published July 22.)

3. Google Ads scripts: Everything you need to know

Streamline campaign management with Google Ads scripts. Get insights, use cases, and practical tips for using automation to boost performance. (By Frederick Vallaeys. Published Jan. 9.)

2. PPC in the age of zero-click search: How to stay profitable

Fewer clicks mean higher stakes. Win visibility with precise targeting, value-based bidding, and authority across paid and organic search. (By Sarah Stemen. Published Oct. 7.)

1. 5 Google Ads tactics to drop in 2026

Some PPC practices no longer fit today’s automated Google Ads environment. Here’s what to phase out – and what to prioritize next year. (By Sarah Vlietstra. Published Nov. 4.)

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How to craft great page titles for SEO?

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Writing strong page titles is one of the simplest and most impactful SEO optimizations you can make. The title tag is often the first thing users see in search results, and it helps search engines understand the content of your page.

In this article, you’ll learn what SEO page titles are, why they matter, and how to write titles that improve visibility and attract clicks.

Key takeaways

  • Crafting a strong page title is vital for SEO; it attracts clicks and helps search engines understand your content
  • An SEO page title appears in search results and browser tabs, serving as the first impression for users
  • To optimize your page title, include relevant keywords and ensure it aligns with the content to improve your ranking
  • Yoast SEO provides tools to help check title width and keyword usage, and includes an AI-powered title generator
  • You can change the page title after publication, and doing so may significantly improve click-through rates

Table of contents

What is an SEO page title?

Let’s start with the basics. If you look at the source of a page (right-click on the page, then choose View Page Source), you find a title in the head section. It looks like this:

This is an example SEO title - Example.com

This is the HTML title tag, also called the SEO title. When you look something up in a search engine, you get a list of results that appear as snippets. The part that looks like a headline is the SEO title. The SEO title typically includes the post title but may also incorporate other elements, such as the site name. Or even emojis!

An example of a Google snippet with a favicon, site name, URL, meta description, and title in the largest font

In most cases, the SEO title is the first thing people see, even before they get on your site. In tabbed browsers, you will usually also see the SEO title in the page tab, as shown in the image below.

An SEO title in a browser tab

What’s the purpose of an SEO title?

Your SEO title aims to entice people to click on it, visit your website, read your post, or purchase your product. If your title is not good enough, people will ignore it and move on to other results. Essentially, there are two goals that you want to achieve with an SEO title:

  1. It must help you rank for a keyword
  2. It must make the user want to click through to your page

Google uses many signals when deciding your relevance for a specific keyword. While click-through rate is not a direct ranking factor, user interaction with search results can be a signal that a result matches search intent.

If your page ranks well but attracts few clicks, that may indicate your title doesn’t resonate with searchers. Improving your SEO title can increase clicks and help you perform better over time.

Additionally, as mentioned earlier, Google uses the SEO title specified for your website as a ranking input. So, it’s not just about those clicks; you also need to ensure that your title reflects the topic being discussed on your page and the keyword that you’re focusing on. The SEO title you use has a direct influence on your ranking.

Now that you know the importance of SEO titles, let’s look at how to evaluate and improve them. Tools like Yoast SEO (Free) can help by checking key elements such as title width and keyword usage. Yoast SEO Premium uses generative AI to create titles.

A smarter analysis in Yoast SEO Premium

Yoast SEO Premium has a smart content analysis that helps you take your content to the next level!

Get Yoast SEO Premium Only $118.80 / year (ex VAT)

Yoast SEO Premium includes an AI-powered title generator that can help you create SEO-friendly page titles based on your content and focus keyphrase. This can be useful for inspiration or for quickly generating alternatives when you’re unsure how to phrase a title.

As with any AI-generated content, it’s best to review and refine the suggested titles to ensure they align with your page’s intent, brand voice, and audience expectations.

In addition, if you use Yoast SEO Premium, you get various other AI features, like Yoast AI Optimize, that help you do the hard work.

Simply hit the Use AI button to have Yoast SEO Premium generate great titles for you

What does the empty title check in Yoast SEO do?

The empty title check in Yoast SEO Premium is self-explanatory: it checks whether you’ve filled in any text in your post’s ‘Title’ section. If you haven’t, you’ll see a red traffic light reminding you to add a title. Once this is filled in, the post title can be automatically added to the SEO title field using the ‘Title’ variable.

You can edit your titles in the Search appearance section of Yoast SEO

Note that your post title is output as an H1 heading. A clear H1 helps users quickly understand what a page is about, improves accessibility for screen readers, and aids search engines in interpreting the page structure. You should only use one H1 heading per page to avoid confusing search engines. Don’t worry; we’ve got a check for multiple H1 headings in Yoast SEO!

What does the SEO title width check in Yoast SEO do?

You will find this check in the SEO tab of the Yoast SEO sidebar or meta box. If you haven’t written an SEO title yet, this will remind you to do so. Additionally, Yoast SEO verifies the width of your SEO title. When it is too long, you will get a warning.

We used to warn you if your SEO title was too short, but we’ve changed that since our Yoast 17.1 release. A title with an optimal width gets you a green traffic light in the analysis. Remember that we exclude the separator symbol and site title from the title width check. We don’t consider these when calculating the SEO title progress bar.

You can find the SEO title width check in the Yoast SEO sidebar or the meta box

How to write an SEO title with an optimal width

If your SEO title doesn’t have the correct width, parts of it may be cut off in Google’s search results. The result may vary, depending on the device you’re using. That’s why you can also check how your SEO title will look in the mobile and desktop search results in the Search appearance section of Yoast SEO. The tool defaults to the mobile version, but you can also switch to view it in the desktop version.

Here’s a desktop result:

The Search appearance in Yoast SEO lets you switch between the mobile and desktop results

And here’s the mobile result for the same URL:

A mobile preview for this particular page

As a general guideline, aim for a title that fully displays on mobile search results, clearly communicates the main topic, and avoids unnecessary filler words. If your title fits visually and still reads naturally, you’re on the right track.

Width vs. Length

Have you noticed that we talk about width rather than length? Why is that? Rather than using a character count, Google has a fixed width for the titles counted in pixels. While your title tags can be long, and Google doesn’t have a set limit on the number of characters you can use, there is a limit on what’s visible in the search results. If your SEO title is too wide, Google will visually truncate it. That might be different from what you want. Additionally, avoid wasting valuable space by keeping the title concise and clear. Additionally, the SEO title often informs other title-like elements, such as the og:title, which also has display constraints.

Luckily, our Search appearance section can help you out! You can fill in your SEO title; our plugin will provide you with immediate feedback. The green line underneath the SEO title turns red when your title is too long. Keep an eye on that and use the feedback to create great headlines.

The Search appearance section in the Yoast SEO for WordPress block editor
The Google preview in Yoast SEO for Shopify

What does the keyphrase in the SEO title check in Yoast SEO do?

This check appears in the SEO tab of the Yoast SEO sidebar in WordPress and Shopify, as well as in the meta box in WordPress. It checks if you’re using your keyphrase in the SEO title of your post or page. This check is intentionally strict because the SEO title plays an important role in signaling a page’s topic to both search engines and users. Since Google uses the title to figure out your page’s topic, not having the focus keyphrase in the SEO title may harm your rankings. Additionally, potential visitors are more likely to click on a search result that matches their query. For optimal results, try to include your keyphrase at the beginning of the SEO title.

This check finds out if you’ve used your focus keyphrase in your SEO title

How to use your keyphrase in the SEO title

Sometimes, when optimizing for a highly competitive keyword, everyone will have the keyword at the beginning of the SEO title. In that case, you can try making it stand out by putting one or two words before your focus keyword, thereby slightly “indenting” your result. In Yoast SEO, if you start your SEO title with “the”, “a”, “who”, or another function word followed by your keyphrase, you’ll still get a green traffic light.

At other times, such as when you have a very long keyphrase, adding the complete keyphrase at the beginning doesn’t make sense. If your SEO title looks weird with the keyphrase at the beginning, try to add as much of the keyphrase as early in the SEO title as possible. But always keep an eye on the natural flow and readability.

How to reduce the chance of Google rewriting your SEO title

Google may rewrite titles when they are overly long, stuffed with keywords, misleading, or inconsistent with the page’s main heading.

To reduce the likelihood of rewrites:

  • Make sure your SEO title closely matches your page’s H1
  • Avoid excessive separators, repetition, or boilerplate text
  • Ensure the title accurately reflects the page content

While rewrites can still happen, clear and concise titles are more likely to be shown as written.

Want to learn how to write text that’s pleasant to read and optimized for search engines? Our SEO copywriting course can help you with that. You can access this course and our other SEO courses with Yoast SEO Premium. This also gives you access to extra features in the Yoast SEO plugin.

Are you struggling with more aspects of SEO copywriting? Don’t worry! We can teach you to master all facets, so you’ll know how to write awesome copy that ranks. Take a look at our SEO copywriting training and try the free trial lessons!

Crafting SEO-friendly page title: FAQs

Are the SEO title and the H1 heading the same?

To be clear, you should not confuse the SEO title with the post title; both serve different purposes and do not have to be the same.

The post title, also known as the H1 heading, is the main heading users see on the page. Its primary role is to help readers understand what the page is about and to add structure to your content. You should always write your H1 with users in mind.

The SEO title is the title that appears in search results and in the browser tab. This title helps search engines understand the topic of your page and influences whether users click on your result.

While the SEO title and H1 can be similar, they do not need to be identical. In WordPress, tools like Yoast SEO allow you to set a separate SEO title, giving you more control over how your page appears in search results without changing the on-page heading.

Should you add your brand to the SEO title?

For quite some time, it was a common practice among some SEOs to omit the site name from the SEO title. The idea was that the “density” of the title mattered, and the site name wouldn’t help with that. Don’t do this. If possible, your SEO title should include your brand, preferably in a recognizable way. If people search for a topic and see your brand several times, even if they don’t click on it the first time, they might click when they see you again on their next page of results.

However, with the site name and favicon updates, be sure to fill in the site settings, upload a favicon, and make general changes to the design of the snippets. This will increase your brand’s visibility in search results. Today, you’ll notice that Google hardly shows your brand name in the snippet’s title. However, Google often has a mind of its own when generating titles to change them for any given reason. The design and function of the SERPs can change at any moment, so we still recommend adding your brand to your titles.

Can you change the SEO title after a page is published?

Yes. You can change the SEO title even after a page has been published, and doing so can improve performance.

At Yoast, we once noticed that although we ranked well for “WordPress security,” the page was not getting as much traffic as expected. We updated the SEO title and meta description to make them more engaging and relevant. As a result, traffic to that page increased by over 30 percent.

The original SEO title was:

WordPress Security • Yoast

We changed it to:

WordPress Security in a few easy steps! • Yoast

This change did not significantly affect rankings, but it did improve click-through rates. The keywords stayed largely the same, but the title became more compelling for searchers.

This shows that optimizing SEO titles after publication can be an effective way to increase traffic, especially if your page already ranks well but receives fewer clicks than expected.

Does Google always use the SEO title you set?

No. Google does not always display the exact SEO title you set in search results.

That said, the HTML title tag is still the most common source Google uses for generating title links. Google Search uses the following sources to automatically determine title links:

  • The <title> tag
  • The main visible heading on the page, such as the
  • Other headings on the page
  • Prominent text styled to stand out
  • Anchor text from internal or external links
  • Structured data related to the website

Google typically selects one title per page and does not change it for different queries.

What does this mean for you? The SEO title you set remains important for ranking and relevance. Even if Google sometimes displays a different version, your title still helps search engines understand the content of your page.

To stay on top of changes, monitor your key pages in Google Search Console, check how titles appear in search results, and watch for shifts in click-through rates.

Can you use the same title for SEO and social media?

You can, but it is often better not to.

What might be a good SEO title isn’t necessarily a good title for social media. In social media, keyword optimization is less important than creating a title that entices people to click. You often don’t need to include the brand name in the title. This is especially true for Facebook and X if you include some branding in your post image. Our social media appearance previews in Yoast SEO Premium and Yoast SEO for Shopify can help you.

If you use Yoast SEO, you can set different titles for Google, Facebook, and X. Enter your SEO title in the snippet editor, then customize the social media titles in the social tab. If you do not set a specific X title, X will use the Facebook title by default.

This flexibility allows you to optimize your titles for both search engines and social platforms without compromise.

The post How to craft great page titles for SEO? appeared first on Yoast.

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Top 10 SEO expert columns of 2025 on Search Engine Land

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Top Search Engine Land SEO columns of 2025

The evolution of search continued to accelerate in 2025.

Between GEO and AI-driven discovery, agents, and new optimization frameworks and tools, SEO experienced another huge year of change.

As always, Search Engine Land helped you make sense of the advances – what was happening, what was coming next, and what truly mattered.

Below are the 10 most-read SEO columns of 2025, written by our outstanding group of subject matter experts.

10. Will GEO replace SEO – or become part of it?

GEO isn’t the death of SEO. It’s what happens when search becomes multi-platform, multi-modal, and powered by AI. (By Roslyn Ayers. Published Aug. 8.)

9. Meet llms.txt, a proposed standard for AI website content crawling

Find out what llms.txt is, how it works, how to think about it, whether LLMs and brands are buying in, and why you should pay attention. (By Rob Garner. Published March 28.)

8. SEO vs. GEO: What’s different? What’s the same?

Learn how SEO and GEO strategies differ – and how combining both can boost your visibility across search engines and AI-driven platforms. (By Dan Taylor. July 28.)

7. How AI Mode and AI Overviews work based on patents and why we need new strategic focus on SEO

Read this deep dive into six patents that reveal how Google’s AI Overviews and AI Mode work – and what it all means for the future of SEO. (By Michael King. June 2.)

6. How to get cited by AI: SEO insights from 8,000 AI citations

From ChatGPT to Gemini, here’s what each AI model trusts – and how strategic content earns visibility in generative search results. (By James Allen. Published May 12.)

5. AI search is booming, but SEO is still not dead

AI search tools are on the rise, but SEO fundamentals remain critical. Learn how the two intersect and what it means for your strategy. (By Lily Ray. Published July 18.)

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4. 11 free Chrome extensions you need for SEO

Don’t rely solely on tools like Search Console or Screaming Frog. Diversify your toolset with these time-saving Chrome extensions. (By Stephanie Wallace. Published Jan. 16.)

3. AI traffic is up 527%. SEO is being rewritten.

AI platforms are transforming discovery. Traffic is surging. Now strategies must evolve, according to the 2025 Previsible AI Traffic Report.. (By David Bell. Published Aug. 5.)

2. AI optimization: How to optimize your content for AI search and agents

To be visible, optimize your site with clean HTML, metadata, fast responses, and bot-friendly configurations. (By Jed White. Published Jan. 29.)

1. The end of the web? Goodbye HTML, hello AIDI!

Why the web as we know it may fade and what AI, personal agents, and data interfaces mean for publishers, SEO, and commerce. (By Mario Fischer. Published Nov. 14.)

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Top 10 SEO news stories of 2025

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Top Search Engine Land SEO news stories of 2025

Another year in search has come and gone, and Google called it year three of a 10-year platform shift. In 2025, that shift became impossible to ignore. AI moved from experiments and previews into the core of how search actually works.

Below are the biggest SEO news stories of 2025 on Search Engine Land.

Note: This article doesn’t include any stories related to Google algorithm updates. Barry Schwartz wrote a separate recap on that, which will also publish today.

10. Perplexity ranking factors and systems

Independent researcher Metehan Yesilyurt analyzed browser-level interactions to reveal how Perplexity scores, reranks, and sometimes drops content. He uncovered a three-layer machine-learning reranker for entity searches, manual authority whitelists, and dozens of engagement and relevance signals.

Yesilyurt’s research also found boosts for authoritative domains, strong early performance, and topics centered on tech and AI. Rankings further reflected time decay, interconnected content clusters, and synchronized YouTube trends that increased visibility across platforms.

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9. Google Search Console Query groups

Google added Query groups to the Search Console Insights report. The feature uses AI to cluster similar search queries into clear audience topics and does not affect rankings. It rolled out gradually to high-volume sites and replaced long query lists with topic-level groupings that make performance shifts easier to spot.

8. HubSpot’s SEO collapse

HubSpot’s organic traffic appeared to fall from 13.5 million to 8.6 million in a month, with most of the losses coming from its blog. The drop followed several Google updates, and SEOs publicly pointed to thin, off-topic, traffic-at-all-costs content that drifted beyond HubSpot’s core expertise.

7. SEO vs. GEO

The SEO identity crisis continued as Google dismissed new acronyms like GEO (generative engine optimization) and AEO (answer engine optimization), arguing that good SEO is good GEO, and that the same fundamentals drive AI Overview rankings.

That stance collided with Google’s own admission that search traffic decline is inevitable as AI answers replace clicks, even while traditional search still dominates discovery at a massive scale.

Yet, search behavior is fracturing: users turn to AI for quick answers and to Google for deeper research, pushing brands to optimize for visibility, not just traffic.

6. Google AI Mode

Google rapidly expanded AI Mode from an opt-in experiment into a widely available, and possibly soon default, search experience. It added deeper research, agentic actions, personalization, and Gemini 2.5, signaling longer and more complex search behavior.

At the same time, AI Mode exposed major transparency gaps. It initially broke referral tracking and still blends performance data into standard Search Console reports, raising new concerns about visibility, attribution, and what SEO becomes as AI takes on a larger role in search.

5. Cloudflare vs. Google

Cloudflare CEO Matthew Prince said AI was breaking the web’s search-driven business model. He said Google scraped far more content while sending back much less traffic because of zero-click results. He added that AI companies deepen the imbalance by consuming huge amounts of content with little return to creators, putting original publishing at risk unless the economic model changes.

4. Google search market share dips

Statcounter data showed Google’s global search share fell below 90% in October, November, and December 2024, the first time its search share remained under 90% since early 2015. The decline was driven mainly by Asia, alongside a December U.S. dip to 87.39%. Bing, Yandex, and Yahoo captured much of the lost share.

3. AI-generated content

Google tightened its stance on AI-generated content by telling quality raters to give the Lowest ratings to pages where most main content is auto- or AI-generated with little originality or added value. It also expanded its spam definitions to target scaled, low-effort AI use.

At the same time, Google tested AI-generated and AI-summarized search snippets, pointing to a future where AI both judges content more harshly and increasingly controls how that content appears in search.

2. Google AI Overviews impact on clicks

Analyses from Seer, Ahrefs, Amsive, and BrightEdge all showed the same pattern. Google Search produced more impressions and more AI Overview visibility, but sent fewer clicks. The drop was sharpest on non-branded, informational queries, where AI Overviews pushed classic results down, and CTR fell hard.

The studies also found a winner-take-some dynamic. Brands cited in AI Overviews saw higher paid and organic CTR, while those left out lost ground, showing that AI visibility increasingly drives results.

1. R.I.P., num=100

Google’s removal of the long-standing &num=100 search parameter disrupted SEO data across the industry. It broke rank-tracking tools and coincided with sharp drops in Google Search Console impressions and query counts.

Early analysis showed most sites lost reported visibility, especially beyond Page 1. The change suggested years of inflated metrics from scrapers and a new, possibly more accurate, view of organic performance.

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The 2025 SEO wrap-up: What we learned about search, content, and trust

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SEO didn’t stand still in 2025. It didn’t reinvent itself either. It clarified what actually matters. If you followed The SEO Update by Yoast monthly webinars this year, you’ll recognize the pattern. Throughout 2025, our Principal SEOs, Carolyn Shelby and Alex Moss, cut through the noise to explain not just what was changing but why it mattered as AI-powered search reshaped visibility, trust, and performance. If you missed some sessions or want the full picture in one place, this wrap-up is for you. We’re looking back at how SEO evolved over the year, what those changes mean in practice, and what they signal going forward.

Key takeaways

  • In 2025, SEO shifted its focus from rankings to visibility management, as AI-driven search reshaped priorities
  • Key developments included the rise of AI Overviews, a shift from clicks to citations, and increased importance of clarity and trust
  • Brands needed to prioritize structured, credible content that AI systems could easily interpret to remain visible
  • By December, SEO transformed to retrieval-focused strategies, where success rested on clarity, relevance, and E-E-A-T signals
  • Overall, 2025 clarified that the fundamentals still matter but emphasized the need for precision in content for AI-driven systems

Table of contents

SEO in 2025: month-by-month overview

Month Key evolutions Core takeaways
January AI-powered, personalized search accelerated. Zero-click results increased. Brand signals, E-E-A-T, performance, and schema shifted from optimizations to requirements. SEO expanded from ranking pages to representing trusted brands that machines can understand.
February Massive AI infrastructure investments. AI Overviews pushed organic results down. Traffic dropped while brand influence and revenue held steady. SEO outcomes can no longer be measured by traffic alone. Authority and influence matter more than raw clicks.
March AI Overviews expanded as clicks declined. Brand mentions appeared to play a larger role in AI-driven citation and selection behavior than links alone. Search behavior grew despite fewer referrals. Visibility fractured across systems. Trust and brand recognition became the differentiators for inclusion.
April Schema and structure proved essential for AI interpretation. Multimodal and personalized search expanded. Zero-click behavior increased further. SEO shifted from optimization to interpretation. Clarity and structure determine reuse.
May Discovery spread beyond Google. AI Overviews reached mass adoption. Citations replaced visits as success signals. SEO outgrew the SERP. Presence across platforms and AI systems became critical.
June – July AI Mode became core to search. Ads entered AI answers. Indexing alone no longer offers guaranteed visibility. Reporting lagged behind reality. Traditional SEO remained necessary but insufficient. Resilience and adaptability became essential.
August Visibility without value became a real risk. SEO had to tie exposure to outcomes beyond the number of sessions. Visibility without value became a real risk. SEO had to tie exposure to outcomes beyond sessions.
September AI Mode neared default status. Legal, licensing, and attribution pressures intensified. Persona-based strategies gained relevance. Control over visibility is no longer guaranteed. Trust and credibility are the only durable advantages.
October Search Console data reset expectations. AI citations outweighed rankings. AI search became the destination. SEO success depends on presence inside AI systems, not just SERP positions.
November AI Mode became core to search. Ads entered AI answers. Indexing alone is no longer a guarantee of visibility. Reporting lagged behind reality. Clarity and structure beat scale. Authority decides inclusion.
December SEO fully shifted to retrieval-based logic. AI systems extracted answers, not pages. E-E-A-T acted as a gatekeeper. SEO evolved into visibility management for AI-driven search. Precision replaced volume.

January: SEO enters the age of representation

January set the tone for the year. Not through a single disruptive update, but through a clear signal that SEO was moving away from pure rankings toward something broader. The search was becoming more personalized, AI-driven, and selective about which sources it chose to surface. Visibility was no longer guaranteed just because you ranked well.

Do read: Perfect prompts: 10 tips for AI-driven SEO content creation

From the start of the year, it was clear that SEO in 2025 would reward brands that were trusted, technically sound, and easy for machines to understand.

What changed in January

Here are a few clear trends that began to shape how SEO worked in practice:

  • AI-powered search became more personalized: Search results reflected context more clearly, taking into account location, intent, and behavior. The same query no longer produced the same result for every user
  • Zero-click searches accelerated: More answers appeared directly in search results, reducing the need to click through, especially for informational and local queries
  • Brand signals and reviews gained weight: Search leaned more heavily on real-world trust indicators like brand mentions, reviews, and overall reputation
  • E-E-A-T became harder to ignore: Clear expertise, ownership, and credibility increasingly acted as filters, not just quality guidelines
  • The role of schema started to shift: Structured data mattered less for visual enhancements and more for helping machines understand content and entities

What to take away from January

January wasn’t about tactics. It was about direction.

SEO started rewarding clarity over cleverness. Brands over pages. Trust over volume. Performance over polish. If search engines were going to summarize, compare, and answer on your behalf, you needed to make it easy for them to understand who you are, what you offer, and why you are credible.

That theme did not fade as the year went on. It became the foundation for everything that followed.

Do check out the full recording of The SEO update by Yoast – January 2025 Edition webinar.

February: scale, money, and AI made the shift unavoidable

If January showed where search was heading, February showed how serious the industry was about getting there. This was the month where AI stopped feeling like a layer on top of search and started looking like the foundation underneath it.

Massive investments, changing SERP layouts, and shifting performance metrics all pointed to the same conclusion. Search was being rebuilt for an AI-first world.

What changed in February

As the month unfolded, the signs became increasingly difficult to ignore.

  • AI Overviews pushed organic results further down: AI Overviews appeared in a large share of problem-solving queries, favoring authoritative sources and summaries over traditional organic listings
  • Traffic declined while brand value increased: High-profile examples showed sessions dropping even as revenue grew. Visibility, influence, and brand trust started to matter more than raw sessions
  • AI referrals began to rise: Referral traffic from AI tools increased, while Google’s overall market share showed early signs of pressure. Discovery started spreading across systems, not just search engines

What to take away from February

February made January’s direction feel permanent.

When AI systems operate at this scale, they change how visibility works. Rankings still mattered, but they no longer told the full story. Authority, brand recognition, and trust increasingly influenced whether content was surfaced, summarized, or ignored.

The takeaway was clear. SEO could no longer be measured only by traffic. It had to be understood in terms of influence, representation, and relevance across an expanding search ecosystem.

Catch the full discussion in The SEO Update by Yoast – February 2025 Edition webinar recording.

March: visibility fractured, trust became the differentiator

By March, the effects of AI-driven search were no longer theoretical. The conversation shifted from how search was changing to who was being affected by it, and why.

This was the month where declining clicks, citation gaps, and publisher pushback made one thing clear. Search visibility was fragmenting across systems, and trust became the deciding factor in who stayed visible.

What changed in March

The developments in March added pressure to trends that had already been forming earlier in the year.

  • AI Overviews expanded while clicks declined: Studies showed that AI Overviews appeared more frequently, while click-through rates continued to decline. Visibility increasingly stopped at the SERP
  • Brand mentions mattered more than links alone: Citation patterns across AI platforms varied, but one signal stayed consistent. Brands mentioned frequently and clearly were more likely to surface
  • Search behavior continued to grow despite fewer clicks: Overall search volume increased year over year, showing that users weren’t searching less; they were just clicking less
  • AI search struggled with attribution and citations: Many AI-powered results failed to cite sources consistently, reinforcing the need for strong brand recognition rather than reliance on direct referrals
  • Search experiences became more fragmented: New entry points like Circle to Search and premium AI modes introduced additional layers to discovery, especially among younger users
  • Structured signals evolved for AI retrieval: Updates to robots meta tags, structured data for return policies, and “sufficient context” signals showed search engines refining how content is selected and grounded

Also read: Structured data with schema for search and AI

What to take away from March

March exposed the tension at the heart of modern SEO.

Search demand was growing, but traditional traffic was shrinking. AI systems were answering more questions, but often without clear attribution. In that environment, being a recognizable, trusted brand mattered more than being the best-optimized page.

The implication was simple. SEO was no longer just about earning clicks. It was about earning inclusion, recognition, and trust across systems that don’t always send users back.

Watch the complete recording of The SEO Update by Yoast – March 2025 Edition.

April: machines started deciding how content is interpreted

By April, the focus shifted again. The question was no longer whether AI would shape search, but how machines decide what content means and when to surface it.

After March exposed visibility gaps and attribution issues, April zoomed in on interpretation. How AI systems read, classify, and extract information became central to SEO outcomes.

What changed in April

April brought clarity to how modern search systems process content.

  • Schema has proven its value beyond rankings: Microsoft has confirmed that schema markup helps large language models understand content. Bing Copilot used structured data to generate clearer, more reliable answers, reinforcing the schema’s role in interpretation rather than visual enhancement
  • AI-driven search became multimodal: Image-based queries expanded through Google Lens and Gemini, allowing users to search using photos and visuals instead of text alone
  • AI Overviews expanded during core updates: A noticeable surge in AI Overviews appeared during Google’s March core update, especially in travel, entertainment, and local discovery queries
  • Clicks declined as summaries improved: AI-generated content summaries reduced the need to click through, accelerating zero-click behavior across informational and decision-based searches
  • Content structure mattered more than special optimizations: Clear headings that boost readability, lists, and semantic cues helped AI systems extract meaning. There were no shortcuts. Standard SEO best practices carried the weight

What to take away from April

April shifted SEO from optimization to interpretation.

Search engines and AI systems didn’t just look for relevance. They looked for clarity. Content that was well-structured, semantically clear, and grounded in real entities was easier to understand, summarize, and reuse.

The lesson was subtle but important. You didn’t need new tricks for AI search. You needed content that was easier for machines to read and harder to misinterpret.

Want the full context? Watch the complete The SEO Update by Yoast – April 2025 Edition webinar.

May: discovery spread beyond search engines

By May, it was no longer sufficient to discuss how search engines interpret content. The bigger question became where discovery was actually happening.

SEO started expanding beyond Google. Visibility fractured across platforms, AI tools, and ecosystems, forcing brands to think about presence rather than placement.

What changed in May

The month highlighted how search and discovery continued to decentralize.

  • Search behavior expanded beyond traditional search engines: Around 39% of consumers now use Pinterest as a search engine, with Gen Z leading adoption. Discovery increasingly happened inside platforms, not just through search bars
  • AI Overviews reached mass adoption: AI Overviews reportedly reached around 1.5 billion users per month and appeared in roughly 13% of searches, with informational queries driving most of that growth
  • Clicks continued to give way to citations: As AI summaries became more common, being referenced or cited mattered more than driving a visit, especially for top-of-funnel queries
  • AI-powered search diversified across tools: Chat-based search experiences added shopping, comparison, and personalization features, further shifting discovery away from classic result pages
  • Economic pressure on content ecosystems increased: Industry voices warned that widespread zero-click answers were starting to weaken the incentives for content creation across the web
  • Trust signals faced stricter scrutiny: Updated rater guidelines targeted fake authority, deceptive design patterns, and manufactured credibility

What to take away from May

May reframed SEO as a visibility problem, not a traffic problem.

When discovery happens across platforms, summaries, and AI systems, success depends on how clearly your content communicates meaning, credibility, and relevance. Rankings still mattered, but they were no longer the primary measure of success.

The message was clear. SEO had outgrown the SERP. Brands that focused on authenticity, semantic clarity, and structured information were better positioned to stay visible wherever search happened next.

Watch the full The SEO Update by Yoast – May 2025 Edition webinar to see all insights in context.

June and July: SEO adjusted to AI-first search

By early summer, SEO entered a more uncomfortable phase. Visibility still mattered, but control over how and where content appeared became increasingly limited.

June and July were about adjustment. Search moved closer to AI assistants, ads blended into answers, and traditional SEO signals no longer guaranteed exposure across all search surfaces.

What changed in June and July

This period introduced some of the clearest operational shifts of the year.

  • AI Mode became a first-class search experience: AI Mode was rolled out more broadly, including incognito use, and began to merge into core search experiences. Search was no longer just results. It was conversation, summaries, and follow-ups
  • Ads entered AI-generated answers: Google introduced ads inside AI Overviews and began testing them in conversational AI Mode. Visibility now competes not only with other pages, but with monetized responses
  • Measurement lagged behind reality: Search Console confirmed AI Mode data would be included in performance reports, but without separate filters or APIs. Visibility changed more rapidly than reporting tools could keep pace.
  • Citations followed platform-specific preferences: Different AI systems favored different sources. Some leaned heavily on encyclopedic content, others on community-driven platforms, reinforcing that one SEO strategy would not fit every system
  • Most AI-linked pages still ranked well organically: Around 97% of URLs referenced in AI Mode ranked in the top 10 organic results, showing that strong traditional SEO remained a prerequisite, even if it was no longer sufficient
  • Content had to resist summarization: Leaks and tests showed that some AI tools rarely surfaced links unless live search was triggered. Generic, easily summarized modern content became easier to replace
  • Infrastructure became an SEO concern again: AI agents increased crawl and request volume, pushing performance, caching, and server readiness back into focus
  • Search moved beyond text: Voice-based interactions, audio summaries, image-driven queries, and AI-first browsers expanded how users searched and consumed information

What to take away from June and July

This period forced a mindset shift.

SEO could no longer assume that ranking, indexing, or even traffic guaranteed visibility. AI systems decided when to summarize, when to cite, and when to bypass pages entirely. Ads, assistants, and alternative interfaces now often sit between users and websites more frequently than before.

The conclusion was pragmatic. Strong fundamentals still mattered, but they weren’t the finish line. SEO now requires resilience: content that carries authority, resists simplification, loads fast, and stays relevant even when clicks don’t follow.

By the end of July, one thing was clear. SEO wasn’t disappearing. It was operating under new constraints, and the rest of the year would test how well teams adapted to them.

Missed the session? You can watch the full The SEO Update by Yoast – June 2025 Edition recording here.

August: the gap between visibility and value widened

By August, SEO teams were staring at a growing disconnect. Visibility was increasing, but traditional outcomes were harder to trace back to it.

This was the month when the mechanics of AI-driven search became more transparent and more uncomfortable.

What changed in August

August surfaced the operational realities behind AI-powered discovery.

  • Impressions rose while clicks continued to decline: AI Overviews dominated the results, driving exposure without generating traffic. In some cases, conversions still improved, but attribution became harder to prove
  • The “great decoupling” became measurable: Visibility and performance stopped moving in sync. SEO teams saw growth in impressions even as sessions declined
  • Zero-click searches accelerated further: No-click behavior climbed toward 69%, reinforcing that many user journeys now ended inside search interfaces
  • AI traffic stayed small but influential: AI-driven referrals still accounted for under 1% of traffic for most sites, yet they shaped expectations around answers, speed, and convenience
  • Retrieval logic shifted toward context and intent: New retrieval approaches prioritized meaning, relationships, and query context over keyword matching

Must read: On-SERP SEO can help you battle zero-click results

What to take away from August

August made one thing unavoidable.

It reinforced the reality that SEO could no longer rely on traffic as the primary proof of value. Visibility still mattered, but only when paired with outcomes that could survive reduced clicks and blurred attribution.

The lesson was strategic. SEO needed to connect visibility to conversion, brand lift, or long-term trust, not just sessions. Otherwise, its impact would be increasingly hard to defend.

Didn’t catch the live session? You can still watch the full The SEO Update by Yoast – August 2025 Edition webinar.

September: control, attribution, and trust were renegotiated

September pushed the conversation further. It wasn’t just about declining clicks anymore. It was about who controlled discovery, attribution, and access to content.

This was the month where legal, technical, and strategic pressures collided.

What changed in September

September reframed SEO around governance and credibility.

  • AI Mode moved closer to becoming the default: Search experiences shifted toward AI-driven answers with conversational follow-ups and multimodal inputs
  • The decline of the open web was acknowledged publicly: Court filings and public statements confirmed what many publishers were already feeling. Traditional web traffic was under structural pressure
  • Legal scrutiny intensified: High-profile settlements and lawsuits highlighted growing challenges around training data, summaries, and lost revenue
  • Licensing entered the SEO conversation: New machine-readable licensing approaches emerged as early attempts to restore control and consent
  • Snippet visibility became a gateway signal: AI tools relied heavily on search snippets for real-time answers, making concise, extractable content more critical
  • Persona-based strategies gained traction: SEO began shifting from keyword targeting to persona-driven content aligned with how AI systems infer intent
  • Trust eroded around generic, formulaic, AI writing styles: Formulaic, overly polished AI content raised credibility concerns, reinforcing the need for editorial judgment
  • Measurement tools lost stability again: Changes to search parameters disrupted rank tracking, reminding teams that SEO reporting would remain volatile

What to take away from September

September forced SEO to grow up again.

Control over visibility, attribution, and content use was no longer guaranteed. Trust, clarity, and credibility became the only durable advantages in an ecosystem shaped by AI intermediaries.

The takeaway was sobering but useful. SEO could still drive value, but only when it is aligned with real user needs, strong brand signals, and content that earned its place in AI-driven answers.

Want to dig a little deeper? Watch the full The SEO Update by Yoast – September 2025 Edition webinar.

October: AI search became the destination

October marked a turning point in how SEO performance needed to be interpreted. The data didn’t just shift. It reset expectations entirely.

This was the month when SEO teams had to accept that AI-powered search was no longer a layer on top of results. It was becoming the place where searches ended.

What changed in October

October brought clarity, even if the numbers looked uncomfortable.

  • AI Mode reshaped user behavior: Around a third of searches now involve AI agents, with most sessions staying inside AI panels. Clicks became the exception, not the default
  • AI citations increasingly rivalled rankings: Visibility increasingly depended on whether content was selected, summarized, or cited by AI systems, not where it ranked
  • Search engines optimized for ideas, not pages: Guidance from search platforms reinforced that AI systems extract concepts and answers, not entire URLs
  • Metadata lost some direct control: Tests of AI-generated meta descriptions suggested that manual optimization would carry less influence over how content appears
  • Commerce and search continued to merge: AI-driven shopping experiences expanded, signaling that transactional intent would increasingly be handled inside AI interfaces

What to take away from October

October reframed SEO as presence within AI systems.

Traffic still mattered, but it was no longer the primary outcome. The real question became whether your content appeared at all inside AI-driven answers. Clarity, structure, and extractability replaced traditional ranking gains as the most reliable levers.

From this point on, SEO had to treat AI search as a destination, not just a gateway.

November: structure and credibility decided inclusion

If October reset expectations, November showed what actually worked.

This month narrowed the gap between theory and practice. It became clearer why some content consistently surfaced in AI results, while other content disappeared.

What changed in November

November focused on how AI systems select and trust sources.

  • Structured content outperformed clever content: Clear headings, predictable formats, and direct answers made it easier for AI systems to extract and reuse information
  • Schema supported understanding, not visibility alone: Structured data remained valuable, but only when paired with clean, readable on-page content
  • AI-driven shopping and comparisons accelerated: Product data quality, consistency, and accessibility directly influenced whether brands appeared in AI-assisted decision flows
  • Citation pools stayed selective: AI systems relied on a relatively small set of trusted sources, reinforcing the importance of brand recognition and authority
  • Search tooling evolved toward themes, not keywords: Grouped queries and topic-based insights replaced one-keyword performance views

What to take away from November

November made one thing clear. SEO wasn’t about producing more content or optimizing harder. It was about making content easier to understand and harder to ignore.

Clarity beats creativity. Structure beat scale. Authority determined whether content was reused at all.

This month quietly reinforced the fundamentals that would define SEO going forward.

For a complete breakdown, check out the full The SEO Update by Yoast – October and November 2025 Edition recording.

December: SEO moved from ranking to retrieval

December tied the entire year together.

Instead of introducing new disruptions, it clarified what 2025 had been building toward all along. SEO was no longer primarily about ranking pages. It was about enabling retrieval.

What changed in December

The year-end review highlighted the new reality of SEO.

  • Search systems retrieved answers, not pages: AI-driven search experiences pulled snippets, definitions, and summaries instead of directing users to full articles
  • Literal language still mattered: Despite advances in understanding, AI systems relied heavily on exact phrasing. Terminology choices directly affected retrieval
  • Content structure became mandatory: Front-loaded answers, short paragraphs, lists, and clear sections made content usable for AI systems
  • Relevance replaced ranking as the core signal: Being the clearest and most contextually relevant answer mattered more than traditional ranking factors
  • E-E-A-T acted as a gatekeeper: Recognized expertise, authorship, and trust signals determined whether content was eligible for reuse
  • Authority reduced AI errors: Strong credibility signals helped AI systems select more reliable sources and reduced hallucinated answers

What to take away from December

December didn’t declare the end of SEO. It defined its next phase.

SEO matured into visibility management for AI-driven systems. Success depended on clarity, credibility, and structure, not shortcuts or volume. The fundamentals still worked, but only when applied with discipline.

By the end of 2025, the direction was clear. SEO didn’t get smaller. It got more precise.

Missed the session? You can watch the full The SEO Update by Yoast – December 2025 Edition recording here.

SEO evolved into visibility management for AI-driven search. Precision replaced volume.

2025 didn’t rewrite SEO. It clarified it.

Search moved from ranking pages to retrieving answers. From rewarding volume to rewarding clarity. From clicks to credibility. And from optimization tricks to systems-level understanding.

The fundamentals still matter. Technical health, helpful content, and strong SEO foundations are non-negotiable. But they are no longer the finish line. What separates visible brands from invisible ones now is how clearly their content can be understood, trusted, and reused by AI-driven search systems.

Going into 2026, the goal isn’t to outsmart search engines. It’s to make your expertise unmistakable. Write for humans, structure for machines, and build authority that holds up even when clicks don’t follow.

SEO didn’t get smaller this year. It got more precise. Stay with us for our 2026 verdict on where search goes next.

The post The 2025 SEO wrap-up: What we learned about search, content, and trust appeared first on Yoast.

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AI’s impact on search isn’t a secret (How to talk to execs about the new era of search)

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Don't Avoid Leadership – Featured image

I get it, these are uncertain times. Organic traffic is dropping like a rock, and new referral traffic  coming in from LLMs like ChatGPT barely scratches the surface of what’s been lost. 

The narrative of “traffic is simply coming from a new source” is not accurate. Search and engagement are happening in new ways, but CTRs are dropping significantly across nearly all industries.

It’s no surprise that many in the industry are feeling anxious about the future of SEO and whether AI might eventually render their roles obsolete. Bringing this up with your C-suite team might feel like the last thing you want to do.

But here’s the reality: Now is exactly the time to lean in.

Your leadership team needs to understand what’s happening, and, more importantly, what you’re doing about it. 

Use this moment to educate, align expectations, and map out how your search strategy is evolving to meet the new landscape head on. Schedule the meeting. Start the conversation. 

I’ll walk you through exactly what to do to maximize the value of this very important meeting. 

Don’t avoid leadership — address AI visibility head-on

No, I’m not going to tell you to picture your leadership team in their underwear. That won’t make the conversation easier, it’ll just make it awkward.

What will help is showing up prepared to lead the conversation.

Your customers search everywhere. Make sure your brand shows up.

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Set the tone from the start. Your leadership team will already respect the fact that you’re raising this issue before they assign someone to investigate it. 

Use this opportunity to guide the discussion and provide clarity, not excuses. This isn’t the time to sugarcoat or downplay what’s happening. 

Let’s break down the key points to bring to leadership to provide clarity.

Why SEO is down and how that impacts business

This is your opportunity to lead with facts, not fear. Give an honest recap of the current state of the industry and how it’s affecting your business.

To start, here are a few critical events that may help explain shifts in performance:

  • Tools like ChatGPT, Gemini, and Perplexity are changing user behavior and pulling searches away from Google entirely.
  • Google has since rolled out AI Overviews (AIOs), which are appearing in more and more SERPs and driving fewer clicks to third-party sites. (Reports of -61% reduction in organic CTR have been reported).
  • LLMs are sending some traffic, but it’s a drop in the bucket compared to what’s been lost from traditional search.
  • Bing launched AI-powered search summaries, but the impact was limited due to its smaller market share.

Next, present a clear, data-driven overview of what’s changed at your company and how it’s already affecting your business. If organic traffic is down 30%, own it, and if revenue has dipped as well, own that too.

Keep the conversation grounded in measurable outcomes and alignment with company goals. And confirm in advance with your analytics team that data you are citing (in addition to LLM visibility metrics you are collecting) are accurate.

Here’s data that needs to be shared.

Discuss revenue, leads (or actions marked as key events), and organic traffic data over time, ideally including year-over-year numbers.

These numbers tie the discussion directly to business impact instead of rankings or other vanity metrics. Year-over-year views help distinguish seasonality and industry trends from real performance drops. Identifying these allows leadership to quickly understand when performance went down vs. a soft market (or shift to a new search ecosystem).

Export and review keywords you’ve been tracking. This is valuable for Google and Bing, and additional insights from LLM rank tracking can add more context.

No, I’m not going against my long standing take that rankings shouldn’t be used as a performance metric on their own. However, in situations like these, rankings are incredibly important to understand if the decrease in traffic is purely lost rankings, lost demand, or shifts in how people search.

Export click/impression and CTR data in Google Search Console and Bing Webmaster Tools. Isolate queries/URLs that saw a CTR decrease and determine if those SERPs are now displaying AIOs.

This further demonstrates when performance is truly down or if everyone playing the game has been impacted. If the pages that saw the biggest dips in clicks also display AI overviews, then the impact is likely very similar for your competitors as well. Just another valuable piece of the puzzle.

Once you deliver the current state of the business, questions will follow. Don’t wait to be asked, own the narrative. Explain the broader context, industry-wide shifts, and emerging technologies behind these changes. A few opportunities to consider:

  • Pull traffic estimates and keyword ranking reports for your top competitors. Are they seeing similar results?
  • Review Google Trends and Exploding Topics to identify increasing (or decreasing) demand for topics/products within your industry.
  • Leverage new AI visibility technology/reports to show your brand’s visibility where the conversation/research is happening (LLMs).

Remember, this isn’t about assigning blame. It’s about showing you understand the change in landscape and how it’s impacting overall performance.

What we’ve learned so far and where we’re going

This is the moment to show leadership that you are not just diagnosing a problem, you are actively working toward a solution. They might not love every answer, but they will respect that you are thinking three steps ahead. 

Make it clear that while the rules are changing, your team is already adapting to win in the next era of search. Then be explicit about what you need from them, whether that’s budget, headcount, data support, or cross-functional alignment, so you can actually execute the plan instead of just presenting the problem.

Here are a few ideas to communicate the next plan of attack.

We are working to increase our brand’s presence outside of traditional search, focusing heavily on AI-generated answers and emerging discovery platforms.

That includes tracking which questions matter most to our buyers, understanding where our brand appears today, and prioritizing content, PR, and partnerships to increase our odds of being named in those answers. 

The goal is simple: If people are getting answers without clicking, our brand still needs to show up in the answer. This is done by repetition and consistency in our brand mentions/citations across the web.

We are rethinking content strategy around entities and topics, not just keywords and rankings.

LLMs reward brands that have deep, consistent coverage of a topic and clear signals of expertise. That affects what we publish, how we structure content, and how we collaborate with PR, product, and subject matter experts to build authority over time. This is the 2.0 version of “SEO content” and it won’t be easy, but the results will be worth it.

We are investing in visibility measurement across both traditional and non-traditional search channels.

Google organic traffic is no longer the single source of truth. We are building reporting that accounts for AI surfaces, social discovery, referral ecosystems, and even offline demand, so the broader team sees the full picture instead of assuming “SEO is down, therefore demand is down.” This helps quantify the broader shift in search ecosystems.

AI Overviews are a permanent shift, not a test.

This means resetting traffic baselines, forecasts, and goals to reflect fewer clicks from classic blue links within the SERP. We are not planning our pipeline in the hope that Google turns AI Overviews off, we are planning for a world where this is the new normal.

Some version of “AI Mode” will likely become Google’s default experience in 2026.

If more searches are answered directly in Google’s interface, fewer visitors will hit our site. That changes how many leads or sales we can expect from SEO alone, and it will force us to rethink everything, including budgeting and how we attribute performance across channels.

How we’ll be proactive and adapt to the new search landscape

You’ve explained what’s happening, why it’s happening, and how your team is adapting. Now, make it clear to leadership that to succeed in this shifting landscape, it can’t be done in isolation. You’ll need alignment, resources, and ongoing support.

Use this opportunity to preemptively answer questions like “What do you need from us?” and to shape the path forward. Leaders like nothing more than an actionable plan that they simply have to bless to get done.

Here are some critical needs to outline.

Search success in the AI era looks different, is measured differently than we are used to, and will take time to optimize. 

We should agree up front on realistic timeframes, what leading indicators we will track, and how often we will report back. Rankings, traffic, and last-click revenue will not always move neatly in sync, so leadership needs to be comfortable with a period where we are learning and recalibrating, not just chasing last year’s dashboards.

Executive buy-in is needed to prioritize long-term brand-building alongside short-term performance metrics. 

This means leadership agrees that some SEO and content initiatives will not pay off in this quarter’s reporting but are required to keep the brand visible in search and AI-driven experiences over the next 12 to 24 months. It also means updating KPIs so the team is not punished for investing in assets that compound over time instead of quick, last-click wins.

Budget flexibility to invest in experimental channels, new content formats, and tools that help track AI visibility. 

A portion of the marketing budget will need to be earmarked for testing, for example: new AI visibility tools, structured data implementations, interactive content, and partnerships that increase the odds of being cited in AI answers. The goal is to learn fast, kill what does not work, and scale what does.

Cross-functional collaboration with analytics, product, PR, and content teams needs to happen to shift how we measure and execute organic growth. 

SEO can no longer operate in a silo. We need analytics to help us build new dashboards that track visibility and assisted impact, PR to prioritize stories and placements that feed both search and AI systems, and product and content teams to align roadmaps with the topics and entities that matter most. Without that alignment, we end up with fragmented efforts and noisy data that no one trusts.

This is your moment to lead the AI visibility discussion

You’re not just reacting to change but guiding your organization through it. AI and LLMs are rewriting how people search, discover, and click. This isn’t the time to panic, let alone support the “organic search is dead” rumor. It means the game has changed, and good businesses aren’t afraid. They adapt. 

Part of that strategy is ongoing monitoring. One-time pitches for buy-in are great, but all marketing efforts need to be measured. Set a regular cadence—for example, a monthly AI visibility update metric alongside your “normal” SEO KPIs. 

As AI and LLMs evolve, you can leverage the data you’ve measured to brief leadership on what has changed and how you have adapted to the situation. 

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By getting ahead of the conversation, grounding your message in data, and proposing a realistic path forward, you’re showing exactly the kind of strategic thinking that executives value.

This is no longer only about SEO, it’s about future-proofing how your business earns visibility, trust, and traffic in a radically new environment. It doesn’t matter if that happens on Google, ChatGPT, Reddit, or anywhere else. What’s important is being visible in the spaces where your customers are hanging out. 

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How vibe coding is changing search marketing workflows

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Vibe coding for search marketers

Search marketers are starting to build, not just optimize.

Across SEO and PPC teams, vibe coding and AI-powered development tools are shrinking the gap between idea and execution – from weeks of developer queues to hours of hands-on experimentation. 

These tools don’t replace developers, but they do let search teams create and test interactive content on their own timelines.

That matters because Google’s AI Overviews are pulling more answers directly into the SERP, leaving fewer clicks for brand websites. 

In a zero-click environment, the ability to build unique, useful, conversion-focused tools is becoming one of the most practical ways search marketers can respond.

What is vibe coding?

Vibe coding is a way of building software by directing AI systems through natural language rather than writing most of the code by hand. 

Instead of working line by line, the builder focuses on intent – what the tool should do, how it should look, and how it should respond – while the AI handles implementation.

The term was popularized in early 2025 by OpenAI co-founder Andrej Karpathy, who described a loose, exploratory style of building where ideas are tested quickly, and code becomes secondary to outcomes. 

His framing captured both the appeal and the risk: AI makes it possible to build functional tools at speed, but it also encourages shortcuts that can lead to fragile or poorly understood systems.

Andrej Karpathy on X

Since then, a growing ecosystem of AI-powered development platforms has made this approach accessible well beyond engineering teams. 

Tools like Replit, Lovable, and Cursor allow non-developers to design, deploy, and iterate on web-based tools with minimal setup. 

The result is a shift in who gets to build – and how quickly ideas can move from concept to production.

That speed, however, doesn’t remove the need for judgment. 

Vibe coding works best when it’s treated as a craft, not a shortcut. 

Blindly accepting AI-generated changes, skipping review, or treating tools as disposable experiments creates technical debt just as quickly as it creates momentum. 

Mastering vibe coding means learning how to guide, question, and refine what the AI produces – not just “see stuff, say stuff, run stuff.”

This balance between speed and discipline is what makes vibe coding relevant for search marketers, and why it demands more than curiosity to use well.

Vibe coding vs. vibe marketing

Vibe coding should not be confused with vibe marketing. 

AI no-code tools used for vibe coding are designed to build things – applications, tools, and interactive experiences. 

AI automation platforms used for vibe marketing, such as N8N, Gumloop, and Make, are built to connect tools and systems together.

For example, N8N can be used to automate workflows between products, content, or agents created with Replit. 

These automation platforms extend the value of vibe-coded tools by connecting them to systems like WordPress, Slack, HubSpot, and Meta.

Used together, vibe coding and AI automation allow search teams to both build and operationalize what they create.

 Why vibe coding matters for search marketing

The search marketer's guide to vibe coding

In the future, AI-powered coding platforms will likely become a default part of the marketing skill set, much like knowing how to use Microsoft Excel is today. 

AI won’t take your job – but someone who knows how to use AI might. 

We recently interviewed candidates for a director of SEO and AI optimization role.

None of the people we spoke with were actively vibe coding or had used AI-powered development software for SEO or marketing.

That gap was notable. 

As more companies add these tools to their technology stacks and ways of working, hands-on experience with them is likely to become increasingly relevant.

Vibe coding lets search marketers quickly build interactive tools that are useful, conversion-focused, and difficult for Google to replicate through AI Overviews or other SERP features.

For paid search, this means teams can rapidly test interactive content ideas and drive traffic to them to evaluate whether they increase leads or sales. 

These platforms can also be used to build or enhance scripts, improve workflows, and support other operational needs.

For SEO, vibe coding makes it possible to add meaningful utility to pages and websites, which can increase engagement and encourage users to return. 

Returning visitors matter because, according to Google’s AI Mode patent, user state – which includes engagement – plays a significant role in how results are generated in AI Overviews and AI Mode.

Google’s AI Mode patent - Sheet 9 of 11

For agency founders, CEOs, CFOs, and other group leaders, these tools also make it possible to build custom internal systems to support how their businesses actually operate. 

For example, I used Replit to build an internal growth forecasting and management tool.

Internal growth forecasting and management tool - Replit

It allows me to create annual forecasts with assumptions, margins, and P&L modeling to manage the SEO and AI optimization group. 

There isn’t off-the-shelf software that fully supports those needs.

Vibe coding tools can also be cost-effective. 

In one case, I was quoted $55,000 and a three-month timeline to build an interactive calculator for a client. 

Using Replit, I built a more robust version in under a week on a $20-per-month plan.

Beyond efficiency, the most important reason to develop these skills is the ability to teach them. 

Helping clients learn how to build and adapt alongside you is increasingly part of the value agencies provide.

In a widely shared LinkedIn post about how agencies should approach AI, Chime CMO Vinneet Mehra argued that agencies and holding companies need to move from “we’ll do it for you” to “we’ll build it with you.” 

In-house teams aren’t going away, he wrote, so agencies need to partner with them by offering copilots, playbooks, and embedded pods that help brands become AI-native marketers.

Being early to adopt and understand vibe coding can become a competitive advantage. 

Used well, it allows teams to navigate a zero-click search environment while empowering clients and strengthening long-term working relationships – the kind that make agencies harder to replace.

Top vibe coding platforms for search marketers

There are many vibe coding platforms on the market, with new ones continuing to launch as interest grows. Below are several leading options worth exploring.

AI development tool
and experience level		 Pros Cons
Google AI Studio
(Intermediate)		 • Direct access to Google’s latest Gemini models.
• Seamless integration with Google ecosystem (Maps, Sheets, etc.).
• Free tier available for experimentation.		 • Locked into Google’s ecosystem and Gemini models.
• Limited flexibility compared to open platforms.
• Smaller community/resources compared to established tools.
Lovable
(Beginner)		 • Rapid full-stack app generation from natural language.
• Handles database setup automatically. 
• Minimal coding knowledge required.		 • Relatively new platform with less maturity.
• Limited customization for complex applications.
• Generated code may need refinement for production.
Figma Make
(Intermediate)		 • Seamless design to code workflow within.
• Ideal for teams already using Figma.
• Bridges gap between designers and developers.		 • Requires Figma subscription and ecosystem.
• Newer tool, still evolving features.
• Code output may need developer review for production.
Replit
(Intermediate)		 • All-in-one platform (code, deploy, host).
• Strong integration capabilities with third-party tools.
• No local setup required.		 • Performance can lag compared to local development.
• Free tier has significant limitations.
• Fees can add up based on usage.
Cursor
(Advanced)		 • Powerful AI assistance for experienced developers.
• Works locally with your existing workflow.
• Advanced code understanding and generation.		 • Steeper learning curve, requires coding knowledge.
• Need to download the software GitHub dependency for some features.

For beginners:

  • Lovable is the most user-friendly option for those with little coding experience. 
  • Figma Make is also intuitive and works well for teams already using Figma. 
  • Replit is also relatively easy to use and does not require prior coding experience.

For developers, Replit and Cursor offer deeper tooling and are better suited for integrations with other systems, such as CRMs and CMS platforms.

Google AI Studio is broader in scope and offers direct connections to Google products, including Google Maps and Gemini, making it useful for teams working within Google’s ecosystem.

You should test several of these tools to find the one that best fits your needs. 

I prefer Replit, but I will be using Figma Make because our creative teams already work in Figma. 

Bubble is also worth exploring if you are new to coding, while Windsurf may be a better fit for more advanced users.

Practical SEO and PPC applications: What you can build today

There is no shortage of things you can build with vibe coding platforms. 

The more important question is what interactive content you should build – tools that do not already exist, solve a real problem, and give users a reason to return. 

Conversion focus matters, but usefulness comes first.

Common use cases include:

  • Lead generation tools
    • Interactive calculators, such as ROI estimators and cost analyzers.
    • Quiz funnels with email capture.
    • Free tools, including word counters and SEO analyzers
  • Content optimization tools
    • Keyword density checkers.
    • Readability analyzers.
    • Meta title and description generators
  • Conversion rate optimization
    • Product recommenders.
    • Personalization engines.
  • Data analysis and reporting
    • Custom analytics dashboards.
    • Rank tracking visualizations.
    • Competitor analysis scrapers, with appropriate ethical considerations.

Articles can only take you so far in a zero-click environment, where AI Overviews increasingly provide direct answers and absorb traffic. 

Interactive content should be an integral part of a modern search and content strategy, particularly for brands seeking to enhance visibility in both traditional and generative search engines, including ChatGPT. 

Well-designed tools can earn backlinks, increase time on site, drive repeat visits, and improve engagement signals that are associated with stronger search performance.

For example, we use AI development software as part of the SEO and content strategy for a client serving accounting firms and bookkeeping professionals. 

Our research led to the development of an AI-powered accounting ROI calculator designed to help accountants and bookkeeping firms understand the potential return on investment from using AI across different parts of their businesses.

The calculator addresses several core questions:

  • Why AI adoption matters for their firm.
  • Where AI can deliver the most impact.
  • What the expected ROI could be.

It fills a gap where clear answers did not previously exist and represents the kind of experience Google AI Overviews cannot easily replace.

AI adoption ROI calculator

The tool is educational by design. 

AI ROI calculator for accounting firms

It explains which tasks can be automated with AI, displays results directly on screen, forecasts a break-even point, and allows users to download a PDF summary of their results.

AI ROI calculator features

AI development software has also enabled us to design additional calculators that deliver practical value to the client’s target audience by addressing problems they cannot easily solve elsewhere.

Get the newsletter search marketers rely on.

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A 7-step vibe coding process for search marketers

Vibe coding works best when it follows a structured workflow. 

The steps below outline a practical process search marketers can use to plan, build, test, and launch interactive tools using AI-powered development platforms.

Step 1: Research and ideation

Run SERP analysis, competitor research, and customer surveys, and use audience research tools such as SparkToro to identify gaps where AI Overviews leave room for interactive tools. 

Include sales, PR, legal, compliance, and cybersecurity teams early in the process. 

That collaboration is especially important when building tools for clients. 

When possible, involve customers or target audiences during research, ideation, and testing.

Step 2: Create your content specification document

Create a content specification document to define what you want to build before you start. 

This document should outline functionality, inputs, outputs, and constraints to help guide the vibe coding software and reduce errors. 

Include as much training context as possible, such as brand colors, tone of voice, links, PDFs, and reference materials. 

The more detail provided upfront, the better the results.

Use this Interactive Content Specification Brief template, and review the instructions before getting started.

Step 3: Design before functionality

Begin with wireframes and front-end design before building functionality. 

Replit prompts for this approach during setup, and it helps reduce rework later. 

Getting the design close to final before moving into logic makes it easier to evaluate usability. 

Design changes can always be made later.

Step 4: Prompt like a product manager

After submitting the specification document, continue prompting to refine the build. 

Ask the AI why it made specific decisions and how changes affect the system. 

In practice, targeted questions lead to fewer errors and more predictable outcomes.

Prompt like a product manager

Step 5: Deploy and test

Deploy the tool to a test URL to confirm it behaves as expected.

If the tool will be embedded on other sites, test it in those environments as well. 

Security configurations can block API calls or integrations depending on the host site. 

I encountered this when integrating a Replit build with Klaviyo. 

After reviewing the deployment context, the issue was resolved.

Step 6: Update the content specification document

Have the AI update the content specification document to reflect the final version of what was built. 

This creates a record of decisions, changes, and requirements and makes future updates or rebuilds easier. 

Save this document for reference.

Step 7: Launch

Push the interactive content live using a custom domain or by embedding it on your site. 

Plan distribution and promotion alongside the launch. 

This is why involving PR, sales, and marketing teams from the beginning of the project matters.

They play a role in ensuring the content reaches the right audience.

The dark side of vibe coding and important watchouts

Vibe coding tools are powerful, but understanding their limitations is just as important as understanding their strengths. 

The main risks fall into three areas: 

  • Security and compliance.
  • Price creep.
  • Technical debt.

Security and compliance 

While impressive, vibe coding tools can introduce security gaps. 

AI-generated code does not always follow best practices for API usage, data encryption, authentication, or regulatory requirements such as GDPR or ADA compliance. 

Any vibe-coded tool should be reviewed by security, legal, and compliance professionals before launch, especially if it collects user data. 

Privacy-by-design principles should also be documented upfront in the content specification document.

These platforms are improving. 

For example, some tools now offer automated security scans that flag issues before deployment and suggest fixes. 

Even so, human review remains essential.

Price creep

Another common risk is what could be described as the “vibe coding hangover.” 

A tool that starts as a quick experiment can quietly become business-critical, while costs scale alongside usage. 

Monthly subscriptions that appear inexpensive at first can grow rapidly as traffic increases, databases expand, or additional API calls are required.

In some cases, self-hosting a vibe-coded project makes more sense than relying on platform-hosted infrastructure. 

Hosting independently can help control costs by avoiding per-use or per-visit charges.

Technical debt

Vibe coding can also create technical debt. 

Tools can break unexpectedly, leaving teams staring at code they no longer fully understand – a risk Karpathy highlighted in his original description of the approach. 

This is why “Accept all” should never be the default. 

Reviewing AI explanations, asking why changes were made, and understanding tradeoffs are critical habits.

Most platforms provide detailed change logs, version history, and rollback options, which makes it possible to recover when something breaks. 

Updating the content specification document at major milestones also helps maintain clarity as projects evolve.

Vibe coding is your competitive edge

AI Overviews and zero-click search are changing how value is created in search. 

Traffic is not returning to past norms, and competing on content alone is becoming less reliable. 

The advantage increasingly goes to teams that build interactive experiences Google cannot easily replicate – tools that require user input and deliver specific, useful outcomes.

Vibe coding makes that possible. 

The approach matters: start with research and a clear specification, design before functionality, prompt with intent, and iterate with discipline. 

Speed without structure creates risk, which is why understanding what the AI builds is as important as shipping quickly.

The tools are accessible. Lovable lowers the barrier to entry, Cursor supports advanced workflows, and Replit offers flexibility across use cases. 

Many platforms are free to start. The real cost is not testing what’s possible.

More importantly, vibe coding shifts how teams work together. 

Agencies and in-house teams are moving from “we’ll do it for you” to “we’ll build it with you.” 

Teams that develop this capability can adapt to a zero-click search environment while building stronger, more durable partnerships.

Build something. Learn from it. The competitive advantage is often one prompt away.

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Localized SEO for LLMs: How Best Practices Have Evolved

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Large language models (LLMs) like ChatGPT, Perplexity, and Google’s AI Overviews are changing how people find local businesses. These systems don’t just crawl your website the way search engines do. They interpret language, infer meaning, and piece together your brand’s identity across the entire web. If your local visibility feels unstable, this shift is one of the biggest reasons.

Traditional local SEO like Google Business Profile optimization, NAP consistency, and review generation still matter. But now you’re also optimizing for models that need better context and more structured information. If those elements aren’t in place, you fade from LLM-generated answers even if your rankings look fine. When you’re focusing on a smaller local audience, it’s essential that you know what you have to do.

Key Takeaways

  • LLMs reshape how local results appear by pulling from entities, schema, and high-trust signals, not just rankings.
  • Consistent information across the web gives AI models confidence when choosing which businesses to include in their answers.
  • Reviews, citations, structured data, and natural-language content help LLMs understand what you do and who you serve.
  • Traditional local SEO still drives visibility, but AI requires deeper clarity and stronger contextual signals.
  • Improving your entity strength helps you appear more often in both organic search and AI-generated summaries.

How LLMs Impact Local Search

Traditional local search results present options: maps, listings, and organic rankings. 

Search results for "Mechanic near Milkwaukee."

LLMs don’t simply list choices. They generate an answer based on the clearest, strongest signals available. If your business isn’t sending those signals consistently, you don’t get included.

An AI overview for "Where can I find a good mechanic near Milkwaukee?"

If your business information is inconsistent and your content is vague, the model is less likely to confidently associate you with a given search. That hurts visibility, even if your traditional rankings haven’t changed. As you can see above, these LLM responses are the first thing that someone can see in Google, not an organic listing. This doesn’t even account for the growing number of users turning to LLMs like ChatGPT directly to answer their queries, never using Google at all.

How LLMs Process Local Intent

LLMs don’t use the same proximity-driven weighting as Google’s local algorithm. They infer local relevance from patterns in language and structured signals.

They look for:

  • Reviews that mention service areas, neighborhoods, and staff names
  • Schema markup that defines your business type and location
  • Local mentions across directories, social platforms, and news sites
  • Content that addresses questions in a city-specific or neighborhood-specific way

If customers mention that you serve a specific district, region, or neighborhood, LLMs absorb that. If your structured data includes service areas or specific location attributes, LLMs factor that in. If your content references local problems or conditions tied to your field, LLMs use those cues to understand where you fit. 

This is important because LLMs don’t use GPS or IP address at the time of search like Google does. They are reliant on explicit mentions and pull conversational context, IP-derived from the app to get a general idea, so it’s not as proximity-exact relevant to the searcher.

These systems treat structured data as a source of truth. When it’s missing or incomplete, the model fills the gaps and often chooses competitors with stronger signals.

Why Local SEO Still Matters in an AI-Driven World of Search

Local SEO is still foundational. LLMs still need data from Google Business Profiles, reviews, NAP citations, and on-site content to understand your business. 

NAP info from the better business bureau.

These elements supply the contextual foundation that AI relies on.

The biggest difference is the level of consistency required. If your business description changes across platforms or your NAP details don’t match, AI models sense uncertainty. And uncertainty keeps you out of high-value generative answers. If a user has a more specific branded query for you in an LLM, a lack of detail may mean outdated/incorrect info is provided about your business.

Local SEO gives you structure and stability. AI gives you new visibility opportunities. Both matter now, and both improve each other when done right.

Best Practices for Localized SEO for LLMs

To strengthen your visibility in both search engines and AI-generated results, your strategy has to support clarity, context, and entity-level consistency. These best practices help LLMs understand who you are and where you belong in local conversations.

Focus on Specific Audience Needs For Your Target Areas

Generic local pages aren’t as effective as they used to be. LLMs prefer businesses that demonstrate real understanding of the communities they serve.

Write content that reflects:

  • Neighborhood-specific issues
  • Local climate or seasonal challenges
  • Regulations or processes unique to your region
  • Cultural or demographic details

If you’re a roofing company in Phoenix, talk about extreme heat and tile-roof repair. If you’re a dentist in Chicago, reference neighborhood landmarks and common questions patients in that area ask.

The more local and grounded your content feels, the easier it is for AI models to match your business to real local intent.

Phrase and Structure Content In Ways Easy For LLMs to Parse

LLMs work best with content that is structured clearly. That includes:

  • Straightforward headers
  • Short sections
  • Natural-language FAQs
  • Sentences that mirror how people ask questions

Consumers type full questions, so answer full questions.

Instead of writing “Austin HVAC services,” address:
“What’s the fastest way to fix an AC unit that stops working in Austin’s summer heat?”

Google results for "What's the fastest way to fix an AC unit thtat stops working in Austin's summer heat?"

LLMs understand and reuse content that leans into conversational patterns. The more your structure supports extraction, the more likely the model is to include your business in summaries.

Emphasize Your Localized E-E-A-T Markers

LLMs evaluate credibility through experience, expertise, authority, and trust signals, just as humans do.

Strengthen your E-E-A-T through:

  • Case details tied to real neighborhoods
  • Expert commentary from team members
  • Author bios that reflect credentials
  • Community involvement or partnerships
  • Reviews that speak to specific outcomes

LLMs treat these details as proof you know what you’re talking about. When they appear consistently across your web presence, your business feels more trustworthy to AI and more likely to be recommended.

Use Entity-Based Markup

Schema markup is one of the clearest ways to communicate your identity to AI. LocalBusiness schema, service area definitions, department structures, product or service attributes—all of it helps LLMs recognize your entity as distinct and legitimate.

An example of schema markup.

Source

The more complete your markup is, the stronger your entity becomes. And strong entities show up more often in AI answers.

Spread and Standardize Your Brand Presence Online

LLMs analyze your entire digital footprint, not just your site. They compare how consistently your brand appears across:

  • Social platforms
  • Industry directories
  • Local organizations
  • Review sites
  • News or community publications

If your name, address, phone number, hours, or business description differ between platforms, AI detects inconsistency and becomes less confident referencing you. It’s also important to make sure more subjective factors like your brand voice and value propositions are also consistent across all these different platforms.

One thing that you may not be aware of is that ChatGPT uses Bing’s index, so Bing Places is one area to prioritize building your presence. While it’s not necessarily going to mirror how Bing will display in the search engine, it uses the data. Things like Apple Maps, Google Mps, and Waze are also priorities to get your NAP info.

Standardization builds authority. Authority increases visibility.

Use Localized Content Styles Like Comparison Guides and FAQs

LLMs excel at interpreting content formats that break complex ideas into digestible pieces.

Comparison guides, cost breakdowns, neighborhood-specific FAQs, and troubleshooting explainers all translate extremely well into AI-generated answers. These formats help the model understand your business with precision.

A comparison between two plumbing services.

If your content mirrors the structure of how people search, AI can more easily extract, reuse, and reference your insights.

Internal Linking Still Matters

Internal linking builds clarity, something AI depends on. It shows which concepts relate to each other and which topics matter most.

Connect:

  • Service pages to related location pages
  • Blog posts to the services they support
  • Local FAQs to broader category content

Strong internal linking helps LLMs follow the path of your expertise and understand your authority in context.

Tracking Results in the LLM Era

Rankings matter, but they no longer tell the full story. To understand your AI visibility, track:

  • Branded search growth
  • Google Search Console impressions
  • Referral traffic from AI tools
  • Increases in unlinked brand mentions
  • Review volume and review language trends

This is easier with the advent of dedicated AI visibility tools like Profound. 

The Profound Interface.

The goal here is to have a method to reveal whether LLMs are pulling your business into their summaries, even when clicks don’t occur.

As zero-click results grow, these new metrics become essential.

FAQs

What is local SEO for LLMs?

It’s the process of optimizing your business so LLMs can recognize and surface you for local queries.

How do I optimize my listings for AI-generated results?

Start with accurate NAP data, strong schema, and content written in natural language that reflects how locals ask questions.

What signals do LLMs use to determine local relevance?

Entities, schema markup, citations, review language, and contextual signals such as landmarks or neighborhoods.

Do reviews impact LLM-driven searches?

Yes. The language inside reviews helps AI understand your services and your location.

Conclusion

LLMs are rewriting the rules of local discovery, but strong local SEO still supplies the signals these models depend on. When your entity is clear, your citations are consistent, and your content reflects the real needs of your community, AI systems can understand your business with confidence.

These same principles sit at the core of both effective LLM SEO and modern local SEO strategy. When you strengthen your entity, refine your citations, and create content grounded in real local intent, you improve your visibility everywhere—organic rankings, map results, and AI-generated answers alike.

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